最新刊期
- “在海陆交接区域，记者报道了一种基于机载海洋激光雷达红外激光的海陆混合波形识别方法，为提取水边线精确位置提供新方案。”
摘要：Ocean–land interfaces (OLIs) are instantaneous boundaries between fluctuating ocean surfaces and land and provide fundamental information for scientific research on ocean hydrology, ocean–land resource management, and sea level rise. However, detecting OLIs with high accuracy and resolution in automated manners is a challenging task. Airborne oceanic LiDARs (AOLs) are high-resolution, efficient, and flexible measurement systems that can be used for integrated ocean and land measurements. In areas where the ocean meets land, both ocean and land may exist in the laser spot of AOL, resulting in mixed ocean–land waveforms. If these mixed waveforms can be accurately identified, they can be used to detect the precise location of the ocean–land interface.Considering the existence of mixed ocean–land waveforms, a mixed waveform method based on AOL mixed ocean–land waveforms is proposed for ocean–land interface determination in this paper. First, the waveform features of the AOL infrared lasers are extracted, and principal component analysis is performed to reduce redundant features. Second, the waveform features are used to classify the AOL waveforms to obtain a membership matrix, and the Otsu method is used to determine the mixed ocean–land waveforms. Third, the DBSCAN algorithm is used to identify and eliminate misclassified mixed waveforms. Fourth, the PAEK algorithm is applied to smooth the laser points corresponding to the mixed ocean–land waveforms and output the ocean–land interface. Finally, the expression of the infrared laser-radar equation for mixed ocean and land is provided, and a method combining theoretical analysis and measured data verification is used to analyze the differences between ocean, land, and mixed ocean and land waveforms.The correctness and effectiveness of the methods proposed in this paper were verified via raw AOL datasets collected by the Optech CZMIL system. Compared with the traditional AOL elevation threshold method, the proposed AOL mixed waveform method reduced the mean and standard deviation of the ocean–land interface bias by 24.07% and 9.76%, respectively, and improved the SSIM index by 0.031, providing a new approach for detecting the ocean–land interface on tidal flats via AOL.The coexistence of water and land within AOL laser spots generates mixed ocean–land waveforms, and identifying these mixed waveforms has important theoretical and practical value. This study proposes an identification method for infrared laser mixed waveforms based on waveform fuzzy classification and Otsu threshold determination and an ocean–land interface extraction method using those identified mixed waveforms. Furthermore, this study extends the laser-radar equation by proposing an infrared laser-radar equation for infrared laser interactions with mixed ocean and land, providing a theoretical basis for studying mixed infrared laser waveforms. On the basis of this equation, a differential analysis was conducted on the ocean, land, and mixed ocean–land waveforms. The correctness and practicality of the infrared laser-radar equation for mixed ocean and land were verified via visualization analysis results of raw waveform data.关键词：Airborne oceanic LiDAR;infrared laser;ocean–land waveform classification;mixed ocean–land waveform;ocean–land interface51|11|0更新时间：2025-03-03 - “最新研究显示，高光谱影像结合地形、土壤水分和生物量数据，能有效反演天然次生林土壤有机碳含量，为森林土壤质量监测提供技术支持。”
摘要：Forest soil organic carbon (SOC) is a critical indicator of forest soil quality, significantly affecting the growth of forest trees and playing an essential role in the sustainable development of forestry. It is imperative to investigate the potential of utilizing hyperspectral images to accurately determine the SOC in natural secondary forests. This would aid in providing technical assistance for estimating forest soil organic carbon on a long-term and large-scale basis. With a focus on the SOC of natural secondary forests, this study randomly selected a total of 67 samples in the Maoershan Experimental Forest Farm of Northeast Forestry University. Soil samples were collected from three different depths: 0-5cm, 5-15cm, and 15-30cm. The SOC content was measured in each sample, and the mean of the three layers was calculated as the SOC content for the 0-30cm depth. The hyperspectral image of ZY1F was analyzed to calculate the first-order differential, second-order differential, reciprocal logarithm of the spectral curve, and vegetation indices. The recursive feature elimination (RFE) method was then employed to screen the features, taking into account the digital elevation model (DEM), soil moisture, and forest aboveground biomass (AGB) datasets. Three machine learning models, namely random forest (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR), and ordinary least squares regression (OLS) were employed to estimate the SOC content; and the best model was chosen to estimate the SOC at various depths. The results showed that XGBoost had the highest accuracy in various soil depths: the R2 of the soil depth of 0-30cm, 0-5cm, 5-15cm, and 15-30cm are 0.54, 0.54, 0.46, and 0.30, respectively, and the RMSE are 21.28g/kg, 44.25g/kg, 15.72g/kg, and 12.56g/kg, respectively. The average SOC values in the natural secondary forest of Maoershan Forest Farm were estimated to be 67.20g/kg, 88.87g/kg, 46.92g/kg, and 40.12g/kg for the 0-30cm, 0-5cm, 5-15cm, and 15-30cm soil layers, respectively. The SOC concentration in the forest declined as the soil depth increased. Variations in SOC content exist across various forest types, and the SOC is ordered in descending order as follows: broad-leaved forest, mixed coniferous and broad-leaved forest, and coniferous forest. The band information from hyperspectral images enables the estimation of the SOC in forests. However, the large number of bands leads to data redundancy, which in turn reduces the accuracy of the model's estimates. The RFE method can be employed to identify the optimal combination of features, thereby reducing the amount of features and enhancing the accuracy of model estimate. The differential characteristics of the 710-850nm bands in hyperspectral images are extremely beneficial for accurately estimating the SOC in natural secondary forests. Topographic factors exert a more significant influence on the SOC at depths above 15 cm, while soil moisture and AGB have a more pronounced effect on SOC in the 5-15 cm layer compared to other factors. The integration of hyperspectral images with DEM, soil moisture, and AGB data can accurately estimate the SOC content of natural secondary forests. This approach offers valuable support for estimating long-term and large-scale SOC of natural secondary forest based on multi-period hyperspectral images.关键词：natural secondary forest;SOC;hyperspectral;machine learning model;feature selection54|42|0更新时间：2025-02-21 - “海岸带遥感研究进展，分析了国内外现状，探讨了科学技术问题，明确了机遇与挑战，为科学认识海岸带遥感中存在的问题提供参考。”
摘要：The coastal zone is an ecologically crucial zone where land and ocean interact, collecting a vast amount of matter and energy. Currently, it is facing unprecedented challenges from intensified human activities, global climate change, and species invasion. Remote sensing science and technology provide an effective means for comprehensive and systematic monitoring of coastal zone resources, ecology, and environment. However, the understanding of key scientific issues of remote sensing in coastal zone is still not clear, and many issues and challenges in the design of remote sensing sensors, the interaction mechanism between electromagnetic waves and surface objects, remote sensing data processing and information extraction, quantitative remote sensing retrieval of ecological parameters and cross-application in multiple fields of coastal zones. From the uniqueness of the coastal zone and the advantages of remote sensing technology, this paper analyzes the current status of domestic and foreign research on coastal remote sensing using the Web of Science and CNKI databases. It explores the scientific and technological issues facing coastal remote sensing, summarizes research progress, identifies opportunities and challenges, and discusses future development directions. The results show that: (1) Taking into account the characteristics of high spatial and temporal heterogeneity changing climate and complex surface factors in the coastal zone, a new generation payloads with specific spectrum, observation mode, orbit mode and orbital inclination are developed to provide support for large-scale, high-frequency monitoring and fine detection of natural resources in the coastal zone; (2) Accurately analyze the physical and optical characteristics of the coastal zone environment, integrate geoscience big data and numerical simulation technology to develop a high-precision calculation method for the scattering and absorption characteristics of real atmospheric and Marine materials, and clarify the radiation transmission process of the atmospheric, land-water interface in the coastal zone; (3) Develop comprehensive methods for improving remote sensing image quality under complex coastal zone imaging conditions through remote sensing AI model and cloud computing technology, and develop high-precision and intelligent extraction of remote sensing information for the whole coastal zone; (4) Improve the existing radiation transfer model, light energy utilization model and process model, analyze the radiation transfer mechanism of coastal surface elements, build a "mechanism-data" dual driven quantitative inversion model of surface parameters driven by opportunity AI model, and overcome the inversion accuracy errors caused by differences in radiation characteristics of multi-source sensors and inconsistent observation angles and observation times; (5) The cross-application of land-sea integration, ecological restoration and disaster prevention and reduction in the coastal zone has deepened to promote the deep integration of multiple disciplines to form a more comprehensive system of science and technology. This paper can serve as a reference for understanding the scientific challenges of remote sensing in the coastal zone and identifying the direction of remote sensing development in the coastal zone.关键词：coastal zone;remote sensing;review;scientific and technological issues;opportunities;challenges74|55|0更新时间：2025-02-21 - “在生态文明建设领域，专家建立了基于物候的湿地分类体系，并验证了小微湿地提取模型的有效性，为小微湿地提取提供了新思路。”
摘要：Accurately extracting small wetlands over larger areas is of great significance for enhancing the efficiency of wetland monitoring and conservation, as well as further promoting ecological civilization construction. From the perspective of phenology, this paper first established a wetland classification system, including permanent water bodies, flooded herbaceous vegetation, flooded woody vegetation, seasonal inundation areas and paddy fields, based on the characteristics of SAR images, and then constructed the time series of backscatter coefficients and coherence coefficients from Sentinel-1 VV and VH polarization modes. Subsequently, the MultiRocket-RF time series classification model was applied to extract wetlands based on the scattering differences of radar beams from various land cover types during different phenological periods.In addition, based on using 5hm2 as area threshold to divide small wetlands and large wetlands, this paper also proposed a morphological method to alleviate the problem of wetland patch adhesion and reduce the misclassification of small wetlands as large wetlands. Finally, considering that the natural attribute of the extensive wetlands and floodplains, as well as the social development characteristic of being committed to exploring new mechanisms for sustainable development, the Yangtze River Delta Ecological Green Integrated Development Demonstration Area was selected as the study area, the entire year of 2021 was chosen as the sample period. By comparing the performance of the models across various data sources and different sizes of small wetlands, this paper analyzed and verified the effectiveness of the small wetlands extraction method, which combines SAR time-series images with the MultiRocket-RF model.Results show that (1) The combination of SAR time series data and MultiRocket-RF time series classification model could better adapt to the phenology-based wetland classification system, and had excellent performance in extracting various types of wetlands. The overall accuracy reached 93.6%, the Kappa coefficient reached 0.888 and the Macro-F1 score reached 0.804. Notably, the model was particularly advantageous for identifying flooded herbaceous vegetation, flooded woody vegetation and seasonal submerged areas in small wetlands. (2) By applying moderate morphological dilation and erosion to sever the small, narrow, connected wetland patches, the problem of wetland patch adhesion could be effectively alleviated, leading to an improvement in the accuracy of small wetland extraction. Although the Kappa coefficient did not change significantly, the Macro-F1 score increased from 0.798 to 0.804, and the extraction accuracy of various small wetlands in the confusion matrix was generally better. (3) The model was more suitable for extracting small wetlands larger than 1hm2 in this paper. However, due to the speckle noise, geometric distortion, and other negative effects caused by the side-looking characteristics and imaging principles of SAR satellites, the performance of extracting wetlands smaller than 1 hm² was suboptimal, with a significant deviation from the true distribution.关键词：remote sensing;phenology;small wetlands;backscatter coefficient;coherence coefficient;MultiRocket transformation;Random Forest34|31|0更新时间：2025-02-21 - “最新研究利用I-DINCAE模型和DNN校正，有效重构南海海表温度数据，揭示其时空变化特征，为海洋研究提供重要依据。”
摘要：The Sea Surface Temperature (SST) is an important indicator for studying ocean dynamics, ocean-atmosphere interaction and climate change, which is closely related to multiple marine environmental factors such as ocean currents, salinity, and nutrient distribution, collectively affecting the balance and evolution of marine ecosystems. Although the traditional SST acquisition methods are precise, they are limited by the number and coverage of sampling points, making it difficult to meet the requirements of large-scale and high-resolution ocean research. Satellite remote sensing data can cover global waters with high update frequency and is widely used in ocean research. However, during the collection process of satellite remote sensing data, SST data is often missing due to factors such as weather conditions, satellite scanning orbit range, and satellite sensor operation failures, which limits the use of data to some extent. Therefore, precise reconstruction of missing data in satellite remote sensing SST data to obtain high-quality and fully covered sea surface temperature datasets is of great significance for ocean research.This study incorporates the Inception module into a deep convolutional autoencoder (DINCAE) and proposes the I-DINCAE model used for data reconstruction of sea surface temperature products with FY-3C satellite in South China Sea. An improved data interpolation convolutional autoencoder (I-DINCAE) is used to reconstruct the missing SST data in the South China Sea from 2014 to 2020, and the reconstruction accuracy of the DINCAE model and I-DINCAE model is compared and analyzed. In order to further improve the accuracy of SST data, deep neural networks (DNNs) were used to calibrate satellite data in combination with measured data, thereby optimizing the quality of the sea surface temperature dataset. Finally, based on the corrected sea surface temperature data, spatio-temporal variation analysis is conducted to reveal the characteristics of sea surface temperature changes. At the same time, combined with many years of measured data, DNN model is used to calibrate the reconstructed temperature data of the new model. A dataset of 11993 independent measured data points was used for testing, the results show that RMSE, MAE and R² of reconstructed SST and measured SST were 1.27℃, 0.96℃ and 0.84, and after DNN model correction, RMSE decreased to 0.57℃, MAE decreased to 0.43℃ and R² increased to 0.92. Based on the corrected SST data, the spatio-temporal distribution and variation characteristics of SST in the South China Sea at monthly and quarterly scales are analyzed from two dimensions of time and space. The results show that: on the seasonal scale, the SST of the South China Sea has obvious variation characteristics, which shows that the SST reaches the highest value in summer and the SST decreases to the lowest value in winter. On the monthly scale, the variation of SST in the South China Sea presents sine(cosine) wave form, with SST usually reaching a maximum value in June and a minimum value in January. This study not only reveals the uniqueness of the marine environment in the South China Sea, but also provides an important basis for understanding the marine ecosystem and climate change in the South China Sea.关键词：sea surface temperature;data reconstruction;deep learning;FY-3C;spatio-temporal variation26|43|0更新时间：2025-02-21 -
Research on the remote sensing large model for large-scale calculation of land spatial parameters AI导读
“在地理空间智能领域，专家提出土地空间对象化建模框架，为土地空间参数精准解算提供新思路。”
摘要：As a new trend in the development of artificial intelligence (AI), the revolutionary impact of large models (LMs) on scientific research paradigms, production methods, and industrial models cannot be underestimated. Investing in LM research is an inevitable choice. In the field of geographic artificial intelligence (GeoAI), there is still a long way to go between the scientific design and practical application of LMs. This article adheres to the principle of deconstructing complex land surface systems and solving precise land parameters. It proposes to carry out land spatial object-oriented modeling supported by multi-source and multimodal observation data. On this basis, we outline the land spatial parameter system and the solution framework via the integration of five-land-parameters from land use, land cover change, land soil, land resource, land type/application. Furthermore, an intelligent computing remote sensing LM is designed for large-scale parameter solving via integrating three core systems, namely symbol system, perception system, and control system. A preliminary experiment is conducted using the solution of land use parameters in agricultural production spaces as an application case. The practice showed that the proposed framework has great potential in improving the accuracy of large-scale parameter calculation in land space. The proposed model helps to serve the intelligent customization of refined land information products and deepen the understanding of land space. Finally, prospects for LM research on land spatial parameter calculation are presented from the perspectives of model adaptability/robustness, and interpretability/credibility of results.关键词：large model;geospatial artificial intelligence (GeoAI);land spatial object-oriented modeling;land parameter solving;attention mechanism;deep learning network;agricultural production space138|149|0更新时间：2025-01-17 - “在建筑物提取领域，研究者提出了一种双分支网络，有效解决了形状尺度多变和边界提取不准确的问题，为建筑物提取提供了新方案。”
摘要：Objective Remote sensing(RS) image building extraction is one of the research hotspots in the field of RS, which is of great significance for urban planning, illegal building detection, natural disaster assessment and so on. With the rapid development of deep learning technology, it has been introduced into remote sensing image building extraction and achieved significant extraction effect. There are two main challenges for building extraction: 1) The buildings in RS images have different scales and shapes, and 2) it’s difficult to accurately extract building boundaries. Method To address the above two challenges, this paper proposes a two-branch building extraction network integrating main body and edge separation and multi-scale information extraction. First, a main body and edge separation branch (MBESB) is designed for feature decomposition based on the decoupling idea and optical flow estimation technique. MBESB generates the main body and edge features of buildings respectively, thereby enhancing the ability of representing the building boundaries. Then, to fully extract the different-scale features of buildings, a lightweight multi-scale information extraction branch is constructed based on dilated convolution, depth-separable convolution and attention mechanism. Finally, in order to improve the training process of building extraction network, a body-edge-feature-enhanced loss function is presented with the help of the generated main body and edge features. Result Experiments were carried out with two public building extraction datasets, namely the Inria and WHU datasets, in order to evaluate the performance of the proposed MMT-Net method. Five deep learning methods were used as the comparative methods. Quantitative analysis of building extraction results was done on four evaluation metrics, namely precision, recall, F1, and IoU. For the Inria and WHU datasets, the F1/IoU values of the proposed MMT-Net are 0.8894/0.8008 and 0.9567/0.9170, respectively, which are superior to the five comparative methods. Conclusion Experimental results on two commonly used public building extraction datasets show that the proposed building extraction network is feasible and effective. In addition, the ablation experiments’ results indicate that all of the MBESB, the LMIEB and the loss function with auxiliary enhancement of the main body and edge features proposed in this work can enhance the building extraction performance effectively.关键词：remote sensing image;building extraction;deep learning;U-Net;main body and edge separation;two-branch;multi-scale;lightweight107|122|0更新时间：2025-01-02 - “本报道介绍了一种结合深度学习和积雪微波辐射传输模型的降尺度雪深反演算法，为获取区域降尺度雪深产品提供保障。”
摘要：High spatiotemporal resolution snow depth data is crucial for hydrological modeling and disaster forecasting. Currently, high temporal resolution snow depth data is typically derived from passive microwave measurements, but its coarser spatial resolution cannot meet the needs of regional hydrology and disaster research. Based on passive microwave brightness temperature data and combined with high-resolution optical remote sensing data, this paper aims to develop a high-precision downscale snow depth inversion algorithm to provide high spatial and temporal resolution snow depth data for regional-scale hydrology and climate research.This study proposes a downscaling snow depth retrieval algorithm based on multi-source remote sensing data such as passive microwave and optical, coupled with a deep learning model (FT Transformer) and a snow microwave radiative transfer (SMRT) model. Deep learning is used to map the complex nonlinear relationship between features such as AMSR 2 Brightness Temperature Deviation (TBD), Snow Cover Days (SCD) and Snow Cover Fraction (SCF) and snow depth, At the same time, considering the influence of the physical properties of snow, the coupled SMRT is fitted with the effective snow grain size (ESG) to characterize the spatiotemporal dynamic snow properties, and is input into the deep learning model to achieve downscale inversion of snow depth.This algorithm was used to obtain downscaled snow depth data at 500 m spatial resolution in northern Xinjiang.Model training and validation were conducted using observed data from 39 stations in northern Xinjiang. The validation results revealed that Snow Cover Days (SCD) can effectively represent the snow accumulation process. Independent validation showed an 18% improvement in RMSE, indicating enhanced spatial generalization capability of the model. The inclusion of the Effective Snow Grain (ESG) feature significantly improved the overall accuracy of the deep learning-based downscaled snow depth retrieval, resulting in an RMSE of 6.82 cm. This represents a 15% improvement compared to the model without the ESG feature. Additionally, the inclusion of the ESG feature greatly mitigated the underestimation of deep snow (>40 cm), leading to a 35% improvement in RMSE for such conditions. Furthermore, a time series analysis of the snow depth retrieval using the ESG feature demonstrated that it aligns with the observed snow depth variations, thereby constraining and stabilizing the output of the FT-Transformer model. Finally, when compared to existing snow depth products such as AMSR2, ERA5-Land, and SDDsd, the downscaled snow depth data from this study exhibited superior validation accuracy, with an RMSE of 6.51 cm. The spatial distribution of snow depth was also more refined, particularly capturing the complex snow depth heterogeneity in the mountainous regions of northern Xinjiang.This study explored the feasibility of combining the Snow Microwave Radiative Transfer (SMRT) model with deep learning for downscaled snow depth retrieval, It has obtained a downscaled snow depth product with high accuracy performance in northern Xinjiang, providing assurance for the demand of high spatiotemporal resolution snow depth data at the regional scale.关键词：snow depth;Downscaling algorithm;deep learning;SMRT;AMER 2;SCD159|286|0更新时间：2025-01-02 - “在遥感科学研究中，地面观测与遥感数据结合驱动深度学习，创新陆表参数反演范式。但地面观测存在代表性误差，如何获取无限接近遥感像元尺度地面真值是关键。专家围绕像元尺度地面真值获取的关键环节，探讨挑战和解决途径，为提升获取准确性和精度提供新认识和理论指导。”
摘要：(Objective)Ground observation is the foundation of remote sensing scientific research, providing important data support for the construction of quantitative remote sensing models, accurate and efficient inversion of remote sensing information, and validation of remote sensing products. In particular, with the entrance of era of artificial intelligence, ground observation has been combined with satellite data to drive deep learning models, generating remarkable research results in the field of remote sensing. However, with the combination of satellite data with ground observations, uncertainty is unavoidably introduced to the subsequent results and analysis. This is resulted from the representativeness errors of ground observation partly due to the scale differences between ground observations and satellite pixels and partly due to the complex spatial heterogeneity land surface itself. Ground observation only represents the true value of the measured object at the observation time and in the space it represents, but cannot be directly used as the true value at the scale of satellite pixels.(Method)How to improve the spatiotemporal representativeness of ground observations on satellite pixel scales and obtain the closest representation of reality has alway been the key issue in the field of remote sensing experiments. The acquisition of pixel scale ground truth involves the selection of sample areas, evaluation of spatial heterogeneity, optimization of ground sample layout, ground observation, and scale conversion. Although a large amount of research has been carried out for each aspect, there are still cases of conceptual ambiguity and insufficient understanding in each link, resulting in significant uncertainty in obtaining pixel scale ground truth. How to constrain and control the uncertainty of the pixel scale ground truth in the acquisition process and how to obtain the pixel scale ground “truth” with minimum uncertainty is currently a bottleneck problem that urgently needs to be solved. This article discusses the current challenges and possible solutions in obtaining pixel scale ground “truth”, aiming to provide new insights and theoretical guidance for remote sensing field observation experiments.(Result)Large spatial heterogeneity does not necessarily mean poor spatial representativeness of ground observations. Because representativeness error is not only related to spatial heterogeneity, but also to factors such as the number, location, and observation scale of ground stations. Spatial heterogeneity is the dominant factor affecting the representativeness error of ground observations without optimizing sampling. But it is almost unrelated to spatial representativeness error when the sampling was optimized. It is noteworthy that spatial heterogeneity show strong dependence on spatial scales. At a smaller scale, spatial heterogeneity caused by random factors cannot be ignored. As the sub-pixel scale increases, spatial heterogeneity is mainly influenced by structural factors. The influence of geolocation mismatch needs to be fully considered, whose effect can be eliminate by developing the methods to identify the exact spatial extent of validation pixel.(Conclusion)High-quality ground observation data and effective scale conversion methods are essential prerequisites for obtaining high-quality ground "truth" at the pixel scale. However, there is still a lack of high-precision scale conversion methods, especially for complex terrains such as mountainous regions. In terms of ground observations, it is not only necessary to establish a high-quality observation network but also to ensure effective collaboration among different networks, instruments, observation techniques, and data managers to construct a ground observation dataset with a unified quality standard. In terms of scale conversion, there is a need to develop more universal and accurate scale conversion models, aiming to fully utilize ground observation data globally to construct high-quality remote sensing pixel "truth" datasets.关键词：Satellite pixel scale ground truth;ground observation;scale difference;spatial heterogeneity;representativeness errors;uncertainty analysis;validation;accuracy assessment;training sample140|120|0更新时间：2024-12-27 - “在高光谱图像去噪领域，专家提出了基空间非对称拉普拉斯全变分模型，有效降低了噪声，保持了图像质量。”
摘要：Real hyperspectral images (HSI) are vulnerable to high intensity mixed noise, and how to accurately model the noise is very important in the subsequent processing tasks. The method of asymmetric Laplacian noise modeling has achieved a good effect in removing mixed noise, and has been widely studied and applied in the field of HSI denoising. This method takes into account the heavy tail and asymmetry of noise, and models different noises in different bands. However, these methods ignore the inherent distribution characteristics of HSI gradient base space - “地理学第二定律揭示空间数据异质性，地理加权回归技术应运而生，覆盖多层面分析需求，但理论完善仍需努力。”
摘要：According to the second law of geography, spatial heterogeneity or non-stationarity in spatial data and relationships has increasingly drawn more and more attentions in spatial statistics. To explore this fundamental phenomenon, place or location-specific methods and local statistical techniques that assume data relationships to be spatially variant have been extensively developed. In line with the principle of spatial dependence depicted by the first law of geography, the GW regression technique was first proposed to incorporate spatial weights into location-wise regression model calibrations to highlight spatial heterogeneities in data relationships by outputting spatially varying coefficient estimates. With this distance-decaying schema for calculating spatial weights, a series of geographically weighted (GW) models emerge to conduct fine-scaled spatial analysis in terms of descriptive, explanatory, interpretive and predictive scenarios, including GW descriptive statistics, a number of basic GW regression and extensions, GW discriminant analysis, GW principal component analysis, GW machine learning, GW artificial neural network. These GW models form a continually evolving technical framework for identifying spatially non-stationary features or patterns in a variety of disciplines or fields, including geography, social science, biology, public health, and environment science.In this study, we tried to systematically sort out the theoretical and technical framework of GW models. First of all, we summarized the essence and rules for applying the family of GW models, i.e. catering for spatially heterogeneous or non-stationary features and relationships in geographic variables, outputting location-dependent metrics or estimates via calculating spatial weight matrix the distance-decaying principle of spatial dependence presented by Tobler's First Law of Geography. As common and fundamental parts of the GW models, we introduce the hypothesis tests of spatial heterogeneity or non-stationarity, general definition of distance metrics in geography, calculation of spatial weights and bandwidth optimization.Regarding descriptive, explanatory, interpretive, and predictive scenarios, the potential usages of each individual GW model are also discussed from four analysis levels. We recommend univariate GW descriptive statistics, e.g., GW average, GW quantile, GW standard deviation, and GW Skewness, to facilitate users' grasping the spatially heterogeneous distribution of a geographic variable. For exploratory data analysis with multivariate spatial data, GW correlation coefficient and GW principal component analysis could be preferable. GW regression and its rich extensions, specifically multiscale GW regression, provide powerful tools in interpretive analysis and have been widely applied. With data relationships studied comprehensively, accurate predictions usually appear as an ultimate target in data analytics. The usages of GW regression and geographically and temporally weighted regression are straightforward for predictions, and the prediction accuracy is further improved when the artificial intelligence technologies are incorporated, e.g. GW machine learning, GW artificial neural network and geographically neural network weighted regression.The increasing popularity of GW models has resulted in the development of several software packages, standalone programs and toolkits, including the R package GWmodel and GWmodelS, a new, free, user-friendly and high-performance standalone software that incorporates spatial data management and mapping tools as well as the GW model functions. However, there is still a long way to go before GW models being an all-around quantitative analytical framework for spatial heterogeneity due to drawbacks in theoretical foundation, technical completeness and complementarity, and their evolutions to spatio-temporal dimensions.关键词：spatial heterogeneity;Spatial dependence;Quantitive analysis;Spatial non-stationarity;spatial statistics284|482|0更新时间：2024-12-17 - “在稀土矿区生态恢复领域，专家提出了YOLOv8-AS检测方法，有效提升了复垦植被单棵植株的识别和定位能力，为矿区生态恢复提供技术支持。”
摘要：The leaching mining of ion-adsorbed rare earth ore primarily employs in-situ leaching, pile leaching, and pool leaching methods, which result in significant soil pollution. This pollution presents serious environmental challenges, particularly affecting the growth and survival rates of reclaimed vegetation in rare earth mining areas. The restoration of reclaimed vegetation is crucial for mitigating environmental damage and restoring ecological balance. However, the application of intelligent technology to monitor and manage the health and growth of reclaimed vegetation in these mining areas encounters substantial challenges due to the complexities of the natural environment.Unmanned aerial vehicle (UAV) remote sensing image technology has emerged as a promising tool for monitoring and evaluating ecological restoration efforts in rare earth mining areas. The UAV can rapidly capture high-resolution images over large areas, facilitating efficient monitoring of reclaimed vegetation growth in these regions. However, the uneven spatial distribution, varying shapes and diverse overall characteristics of reclaimed vegetation present significant challenges for achieving high-precision automatic recognition from UVA images. Consequently, relying solely on traditional image processing technique for vegetation detection and classification proves to be be inadequate. To address these challenges and enhance the automatic recognition and localization capabilities of individual reclaimed vegetation in UAV images, this paper proposes a method for reclaimed vegetation detection in rare earth mining areas (YOLOv8n), which integrates the global feature YOLOv8-AS. This method represents an innovative improvement over YOLOv8n: first, the downsampling module ADown is introduced to optimize the feature convolution operation, thereby reducing the feature loss during the deep model training process. Second, the SPPF-GFP (Spatial Pyramid Pooling Fast - Global Feature Pool) module is employed for feature extraction, significantly enhancing the detection capability of reclaimed vegetation with substantial variations in overall features.The results showed that in the self-constructed rare earth mining reclamation vegetation dataset, YOLOv8-AS outperforms YOLOv8n by 1.6% and 2.4% in terms of mAP@0.5 and mAP@0.5-0.95, respectively. Compared to YOLOv8n, the model size, number of parameters, and floating point computation of YOLOv8-AS decreased by 11%, 10%, and 9%, respectively. The mAP@0.5 and mAP@0.5-0.95 for the YOLOv8-AS algorithm are 91.1% and 46.8%, respectively. When compared to SSD, Faster R-CNN, RT-DETR, YOLOv5, YOLOv7 and YOLOv7-TINY models regarding mAP@0.5, YOLOv8-AS shows improvements of 14.07%, 23.32%, 1.2%, 2.3%, 3.3%, 2.9% and 1.2%, respectively. According to the comparative experimental results of YOLOV8-AS and YOLOv8 across three scenarios—characterized by a predominance of small targets, simplicity, and complexity—the mAP@0.5-0.95 of YOLOV8-AS increased by 2.3%, 1.2%, and 3%, respectively, when compared to the baseline model YOLOv8. Furthermore, we applied YOLOv8-AS to the reclamation vegetation detection task in a larger scene within a rare earth mining area. The visualization results indicate that, regardless of the scenario—whether featuring numerous small targets, simple scenes, or complex environments—this method significantly enhanced its capacity to identify and accurately locate individual plants in the reclamation vegetation. This finding further substantiates its efficacy in accurately detecting reclaimed vegetation across various conditions. Such advancements are crucial for effectively monitoring the progress of ecological restoration in mining areas and provide essential support for achieving sustainable mining development.关键词：deep learning;object detection;YOLOv8n;UAV Imagery;Rare Earth Mining Area;Reclaimed Vegetation300|120|0更新时间：2024-12-13 - “在红树林资源管理领域，专家提出了一种结合深度学习与传统算法的高精度单木分割框架，为红树林资源调查提供数据支撑。”
摘要：“Objective” Tropical mangroves are one of the most productive and biodiverse forest resources but face several challenges such as monoculture planting structures, low ecosystem quality, low survival rates of planted mangroves, and threats from extreme weather and pests. Conducting surveys on mangrove forest resources provides essential data for the scientific management and conservation of these resources. Accurate segmentation of individual trees is a prerequisite for the inventory of such forest resources. Terrestrial laser scanning (TLS) can provide massive, high-precision, and high-resolution 3D point cloud data. However, the point cloud data are characterized by irregularities, varying densities due to distance, and incompleteness due to occlusions. Furthermore, the mangrove scene is complex with interlaced large and small trees and tree occlusions, making precise individual tree segmentation a considerable challenge. Traditional methods such as local maximum detection based on Canopy Height Models (CHM), have demonstrated good performance in simple plot scenarios. However, in the complex canopy interwoven environments of mangroves, where the upper canopy features are weak, these methods are less effective. Currently, there is a lack of research on individual tree segmentation algorithms for mangroves based on TLS point clouds. To address these issues, we aim to propose an individual tree segmentation algorithm applicable for complex mangrove scenes.“Method” This study innovatively combines deep learning and traditional algorithms to propose a high-precision individual tree segmentation framework for TLS point clouds in complex mangrove scenes. The framework initially employs the deep learning network RandLA-Net for ground filtering and wood-leaf separation. Subsequently, mangrove main stems are segmented using a connected component segmentation method. Finally, individual tree segmentation is achieved through the multiple tree tops constraint module.“Results” To assess the accuracy of the algorithm, we use three measures: completeness, correctness, and accuracy. We also conduct a comparative analysis with two classical algorithms. The experimental results demonstrate that the completeness of the proposed method across different mangrove plots is greater than 0.85, with an average of 0.90; the correctness of the proposed method is greater than the two classic algorithms in four plots; the mean accuracy of the proposed method in different sample plots reaches 0.87, which is significantly higher than the two classic algorithms, thus proving the effectiveness and reliability of our method.“Conclusion” This paper proposes an individual tree segmentation framework for TLS point clouds in complex mangrove scenes. Seven sample plots with various data characteristics were annotated to assess accuracy. The experimental results show that, compared to other algorithms, the proposed method achieved the highest accuracy. Despite the differing characteristics of the sample plots, the overall accuracy of the proposed method exceeded 0.8, demonstrating its effectiveness and robustness.关键词：tropical mangroves;terrestrial laser scanning;point cloud;individual tree segmentation;deep learning320|204|0更新时间：2024-12-12 - “风云四号A星AGRI辐射性能退化，采用DCC方法评估，为定标系数更新提供依据。”
摘要：Objective The advanced geostationary orbit radiometer (AGRI) of FY-4A satellite has been on orbit for 6 years, and the radiation performance of some reflective channels have significantly degraded, affecting the accuracy of quantitative remote sensing product applications. On-orbit vicarious calibration methods based on deep convective cloud (DCC) targets can track and correct the radiometric response of spaceborne optical sensors for attenuation. This method relies on large-sample statistical analysis, and it is significantly to conduct sensitivity studies on factors influencing the calibration accuracy and stability in this method and establish optimal solutions.Method The procedural of the fundamental DCC calibration and tracking method are as follows: initially, extract DCC target pixels from FY-4A/AGRI L1 level data, calculate the reflectance of the target pixels, and apply anisotropic correction using the DCC Angle Distribution Model (ADM). Subsequently, construct daily or monthly Probability Density Functions (PDF) of DCC reflectance and track the trend in peak reflectance (also known as mode) or reflectance mean to monitor and evaluate the radiometric performance of the FY-4A/AGRI instrument. In order to improve the calibration accuracy and stability, the sensitivity research scheme for infrared brightness temperature threshold, pixel uniformity conditions and DCC angle distribution model (ADM) was proposed. Lastly, correct the DCC model and establish an optimal solution according to the results of the sensitivity analysis.Result The results indicate that for the infrared brightness temperature threshold, the sensitivity of DCC mean reflectance is lower than that of probability density function (PDF) peak reflectance in the visible light channel, and in the short-wave infrared channel, the sensitivity of DCC PDF peak reflectance is slightly lower than that of reflectance mean. In the visible-near-infrared band, the CERES ADM model can better correct the effect of DCC reflectance anisotropy, and is significantly better than the Hu model. However, neither of the two ADM models has obvious correction effect in the short-wave infrared band. Based on the above sensitivity studies, the threshold selection and ADM correction strategy in the DCC method are determined. The radiation response of FY-4A/AGRI reflected bands from March 2017 to April 2023 is tracked and evaluated. The results show that the radiation response of 0.47μm, 0.65μm and 2.25μm channels degrades significantly, with the total attenuation rates of 45.55%, 26.22% and 6.362%, respectively. This result provides a reference for updating the AGRI operation calibration coefficient.Conclusion The paper conducted a sensitivity analysis on the key factors in the radiometric calibration tracking method based on Deep Convective Clouds (DCC) for satellite optical sensors, enhancing calibration accuracy and stability through the establishment of an optimal solution. By utilizing optimization methods, it quantitatively evaluated the variations in radiometric response performance in the reflectance band of FY-4A/AGRI, providing valuable reference for updating the operational calibration coefficients of this instrument.关键词：remote sensing and sensors;radiometric calibration;Deep convective cloud;advanced geostationary radiation imager;angular distribution model;top of atmosphere reflectance;reflective solar bands231|116|0更新时间：2024-12-11 - “中国林业科学研究院资源信息研究所发布亚热带林区多平台激光雷达森林样地点云数据集，为森林生态研究提供重要参考。”
摘要：Sharing multi-platform light detection and ranging (LiDAR) point clouds of forests is of great significance for LiDAR remote sensing research and applications in forestry. To this end, the Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, has constructed a multi-platform LiDAR point cloud dataset for forest plots in subtropical regions, featuring airborne laser scanning, unmanned aerial vehicle laser scanning, and terrestrial laser scanning (TLS) point clouds, along with forest inventory data. The dataset was collected at Gaofeng Forest Farm in Guangxi, China, covering 25 plots with three tree species: Eucalyptus, Chinese fir, and Pinus massoniana. The field forest inventory data include plot locations, tree positions, diameter at breast height, tree height, height to the first live branch, and crown width. The dataset enables analysis of forest three-dimensional structural information captured by LiDAR from various platforms, evaluating automated processing algorithms like point cloud registration and tree segmentation. It provides important references for forest research at regional, plot, and tree levels. Additionally, this study developed a ground survey method guided by TLS data. This method utilizes tree stem point clouds to mark tree positions and measures individual trees according to tree maps, improving operational efficiency.关键词：subtropical forest;light detection and ranging;airborne;unmanned aerial vehicle;terrestrial;multi-platform;forest plot;point cloud;reference dataset;field forest inventory509|258|0更新时间：2024-11-26 - “最新研究利用遥感数据和PLUS模型，分析鄂尔多斯市光伏电站土地利用变化，预测未来趋势，为土地规划和资源管理提供科学依据。”
摘要：“Objective” Photovoltaic power stations, which generate electricity using solar radiation, are an important form of clean energy. However, current research generally lacks in-depth investigation into the long-term land use evolution of photovoltaic power stations and has not systematically predicted photovoltaic construction under different policy scenarios. Therefore, it is of significant scientific importance to thoroughly explore the changes in area, type conversion, and spatial distribution characteristics of photovoltaic power stations in Ordos from 2000 to 2023, and on this basis, to predict future land use types under different policy scenarios. “Methodology” To achieve these objectives, this study utilized visual interpretation techniques on satellite imagery from Landsat 5 and GF2, covering the years 2000 to 2023. From these images, land use type maps were generated, which enabled us to track changes in land use over time. Specifically, we examined the spatiotemporal characteristics of photovoltaic power stations, conducting an analysis every five years to determine shifts in spatial patterns. This was done using Gaussian projection ellipses, a method that allowed us to capture the spatial distribution trends of these stations. In addition to this spatial analysis, we employed the PLUS (Patch-generating Land Use Simulation) model, which integrates both natural and socio-economic driving factors to predict future land use patterns under different policy conditions. Key driving factors included population growth, surface temperature, soil heat flux, precipitation, and changes in policy, which are critical elements in understanding the evolution of photovoltaic stations over time and their future development. “Results” The findings of this study are multifaceted and provide valuable insights into the evolution of photovoltaic power stations in Ordos. (1) The overall spatial pattern of land use remained relatively consistent between 2000 and 2011, as well as between 2011 and 2023. However, a noticeable shift occurred starting in 2011, when certain land types, such as desert sand and grassland, began to be converted into photovoltaic power station sites. (2) From 2011 to 2023, there was a clear shift in the spatial distribution of these stations, with the main area of photovoltaic development moving from the northwest to the northeast of Ordos. Additionally, the types of land being used for these constructions evolved, with an increasing trend of converting grassland areas for photovoltaic station use. (3) The analysis using the PLUS model revealed that several key factors were driving these land use changes, including population growth, surface temperature, soil heat flux, precipitation, and, most notably, policy decisions. Policy, in particular, emerged as one of the strongest determinants in the development and expansion of photovoltaic stations in the region. (4) Projections for land use changes in 2030 under three different policy scenarios show that, regardless of the specific scenario, areas allocated to buildings, forests, water bodies, arable land, grassland, and photovoltaic power stations will likely continue to expand. These findings provide important insights into the future changes of photovoltaic power stations in Ordos. “Conclusion” This study sheds light on the spatial and temporal dynamics of photovoltaic power station development in Ordos, highlighting the complex interplay between population growth, environmental factors, and policy decisions in shaping land use changes. The results not only demonstrate how land use has evolved over the past two decades but also provide predictive insights into how it may continue to change by 2030 under different policy scenarios.关键词：Photovoltaic power stations;spatial pattern;spatio-temporal variability;driving factors;scenario simulation514|285|0更新时间：2024-11-06 - “陆地生态系统碳监测卫星激光测高全波形数据在森林冠层高度反演中的应用研究取得进展，为森林资源监测提供解决方案。”
摘要：The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) is China's first remote sensing satellite with space-borne LiDAR as the main payload, which aim at quantitatively monitoring of terrestrial ecosystem carbon storage, forest resources and forest productivity, and serving the goals of "carbon peaking and carbon neutrality" and the monitoring and evaluation of major projects for the protection and restoration of important ecosystems in China. In this paper, the ability of different relative height (RH) metrics to characterize the forest canopy height was evaluated in detail for the full waveform data of the multi-beam LiDAR onboard the Terrestrial Ecosystem Carbon Inventory Satellite. Canopy heights were calculated using RH0, RH5, RH10, RH15, RH20 and last peak locaton as the benchmark. Different percent canopy height determined by RH100, RH95, RH90, RH85 and RH80 were obtained and compared to canopy height model(CHM) generated by airborne LiDAR data to explore the correlation between RH percent canopy height and CMH. The ability in canopy height detection between fixed-gain and variable-gain full-waveform was compared. Moreover, the influence of slope on canopy height extraction was analyzed. Six tracks of multi-beam LiDAR L2B data products from the Terrestrial Ecosystem Carbon Inventory Satellite passing the test area of temperate coniferous-broadleaved mixed forest in Quebec, Canada, were selected for analysis. The results show that the canopy height is overestimated significantly when RH0 is used as the benchmark of canopy height calculation, and the accuracy of canopy height is improved by increasing the background noise threshold with reference to the noise standard deviation, but the effect is limited. As for the fixed-gain waveforms, using RH5 as the benchmark, and identifying the threshold of background noise by 6 times the standard deviation of background noise, the accuracy of different percentage of forest canopy heights achieves RMSEs of between 3.58 m~4.23 m, MEs of less than 1.0 m and MAEs of between 2.52 m~3.21 m, respectively. The accuracy of retrieval canopy heights using variable-gain and fixed-gain full waveform data is comparable, but the times of background noise standard deviation for calculating background noise threshold is smaller than that of fixed gain. Start from RH5 and using proper background noise threshold, the canopy heights retrieved by RH100, RH98, RH95, RH90 and RH85 from both variable-gain and fixed-gain full waveform data were close to the 100% , 95% , 90% , 85% and 80% canopy height from CHM products within the footprint range, respectively. Moreover, using RH5 as the benchmark of canopy height is significantly better than the last peak position derived from waveform decomposition, and is less affected by terrain slope. The configuration of variable and fixed gains is beneficial for enhancing data effectiveness in forest areas. The conclusions will be helpful for the application of the laser altimetry data of Terrestrial Ecosystem Carbon Inventory Satellite in forest canopy height retrieval.关键词：satellite laser altimetry;Terrestrial Ecosystem Carbon Inventory Satellite;Waveform LiDAR;forest canopy height;Relative Height Metrics;Background noise threshold;terrain slope;Canopy Hegiht Model541|165|0更新时间：2024-11-06 -
Hyperspectral image classification network based on Multi-scale Supervised Contrastive Learning AI导读
“在遥感领域，对比学习被广泛应用于高光谱图像分类。针对现有方法的不足，专家提出了基于多尺度监督对比学习的高光谱图像分类网络，实现了精准分类。”
摘要：Objective Hyperspectral image classification, which aims to assign a belonging category to each pixel in a hyperspectral image, is an important application in the field of remote sensing. In recent years, contrastive learning has been widely used in hyperspectral image classification tasks due to its good ability to mine key features of data. However, most of the current self-supervised contrastive learning paradigms use a two-stage scheme to train the network, and it is difficult to avoid defining objects in the same class as negative samples in the pre-training stage, which often leads to a wider intra-class gap. In addition, contrastive learning algorithms generally use data enhancement methods such as cropping and rotating to generate positive samples, and the diversity of generated positive samples is more limited. In this paper, a hyperspectral image classification network based on multi-scale supervised contrastive learning is proposed to solve the above problems. The method aims to extract multiscale spatial features and spectral features level by level, and adaptively fuse the features to generate the final results.1. Method This paper proposes a Multiscale Supervised Contrastive Learning Network (MSCLN) for hyperspectral image classification, which includes two parts: a multiscale contrastive feature learning network and a spatial-spectral hybrid probability-directed fusion classification network. In the multiscale contrast feature learning network, a spectral-guided branch and a spatial feature extraction branch that introduces an attention mechanism are designed to extract spectral-spatial features level by level. Then, two multi-scale spatial features of the same object are constructed as positive samples by introducing label information. Specifically, 2n views can be generated for n objects, in which all views of the same kind of objects are positive samples of each other and the rest are negative samples. Finally, in the spatial-spectral hybrid probability-directed fusion classification network, the learnable parameters are set to integrate the spectral-spatial features to obtain the final classification probability. By co-training the two networks, more accurate classification results can be obtained.2. Result In three public hyperspectral datasets, Houston 2013, WHU-Hi-LongKou and Pavia University, 50, 80 and 50 labeled samples were randomly selected from each category as training sets, respectively. The overall classification accuracy of the proposed algorithm reached 96.20%, 99.20% and 98.96%, respectively, and the classification performance was better than that of the other comparison methods.3. Conclusion The method extracts the discriminative spectral-spatial features hierarchically by MSCLN, and introduces the labelling information to construct the two multiscale spatial features of the same object as positive samples. It makes the same kind of sample distance more aggregated while pushing away the inter-class distance. Finally adaptive fusion of spectral-spatial features to obtain an excellent classification map.4.关键词：hyperspectral images;image classification;contrastive learning;spatial spectral feature fusion;attention mechanism224|326|0更新时间：2024-10-30 - “最新研究提出SSAFormer模型，有效提高红树林群落分类精度，为海洋生态保护提供新方案。”
摘要：(Objective)Mangroves are one of the most biodiverse and productive marine ecosystems, and the fine classification of mangrove communities by combining high-resolution remote sensing images and deep learning has become a hot and difficult topic in current research.(Methods)In this paper, we proposed a novel deep learning classification network model SSAFormer (Swin-Segmentation-Atrous-Transformer) for fine classification of mangrove communities. The SSAFormer used Swin Transformer, a variant of Visual Transformer, as the backbone network. The Atrous Spatial Pyramid Pooling (ASPP) in the Convolutional Neural Network (CNN) architecture was added to the backbone network to extract more scale feature information. The Feature Pyramid Network (FPN) structure was embedded in the lightweight decoder to fuse the rich semantic feature information of the low and high layers. In this paper, three active and passive feature datasets were constructed based on GF-7 multispectral imagery and UAV-LiDAR point clouds, and the classification results of the improved Swin Transformer and SegFormer algorithms were compared and analyzed to further demonstrate the classification performance of the SSAFormer algorithm for mangrove communities.(Result)The results of the study revealed that:(1) Compared with the improved Swin Transformer and SegFormer algorithms, SSAFormer achieved a fine classification of mangroves, with an overall accuracy (OA) increase of 1.77%~ 5.3%, Kappa up to 0.8952, and a mean intersection over union (MIou) was improved by 7.68%;(2) On the GF-7 multispectral dataset, the SSAFormer algorithm achieved the highest overall accuracy (OA) of 91%, and the mean intersection over union (MIou) of the SSAFormer algorithm on the UAV-LiDAR dataset improved to 57.68% on the UAV-LiDAR dataset with the inclusion of spectral features. The mean value of the SSAFormer algorithm mean intersection over union (MIou) improved by 1.48%;(3) The UAV-LiDAR data showed a maximum improvement of 5.35% in the mean intersection over union (MIou) compared to the GF-7 multispectral data, a mean improvement of 1.81% in the overall accuracy(OA), and an improvement of 2.6% in the classification accuracy (F1-score) of the UAV-LiDAR data with the inclusion of spectral features;(4) Based on the SSAFormer algorithm, the highest classification accuracy (F1-score) of 97.07% was achieved for Avicennia marina, the classification accuracy (F1-score) of Aegiceras corniculatum achieved 91.99%, the classification accuracy (F1-score) of Sporobolus alterniflorus reached 93.64%, and the average value of classification accuracy (F1-score) of Aegiceras corniculatum reached the highest 86.91% on SSAFormer model.(Conclusion)The above conclusions proved that the proposed model can effectively improve the classification accuracy of mangrove communities.关键词：mangrove;GF-7 multispectral;UAV-LiDAR point clouds;SSAFormer;deep learning;Active and passive image combination;feature selection;Fine classification of community512|256|0更新时间：2024-10-18 - “风云系列气象卫星搭载的风场测量雷达，采用先进扫描体制，成功获取全球海面风场等参数，性能指标达到预期，为天气预报提供重要数据支持。”
摘要：The Wind Radar (WindRAD) onboard Fengyun-3E (FY-3E) meteorological satellite is the first active remote sensing instrument of China's Fengyun series satellites and the first spaceborne dual frequency & dual polarization scatterometer in the world. Spaceborne scatterometer is important remote sensing instrument for measuring meteorological and ocean parameters, which obtains geophysical parameters such as wind speed and wind direction on the global ocean surface through backscattering measurement of the earth system. WindRAD uses the advanced fan beam with conical scanning system, mainly aiming at measuring the sea surface wind vector all weather and all day with high precision as well as high resolution. In addition, the WindRAD can also measure soil moisture, sea ice and other geophysical parameters. This paper aims to give the preliminary evaluation of in-orbit performance for the WindRAD. The observation principle, signal characteristics and main performance indicators of the WindRAD are introduced, and the detailed data preprocessing method is proposed, that is, the level 1 processing to generate backscattering coefficient of global land and sea surface. According to WindRAD’s in-orbit test after the launch in 2021, the performance of the instrument is preliminarily analyzed. Key telemetry parameters including rotation speed, internal calibration value and temperatures of important components are analyzed. Azimuth resolution, range resolution, observation swath width, radiometric resolution, and internal calibration accuracy are evaluated using WindRAD actual remote sensing data as well as parameters measured before the launch. The analysis results show that WindRAD works steadily in orbit, all of the performance indicators meet the expectations, and can provide high-quality backscattering coefficient data in both C and Ku bands for product retrieval. This work paves the way for WindRAD remote sensing application, assimilation application and weather forecast. WindRAD observation data is received and processed in FY-3E satellite ground system. The operational data is public to the users worldwide and can be obtained from the FENGYUN Satellite Data Center of National Satellite Meteorological Center, China Meteorological Administration (http://satellite.nsmc.org.cn/PortalSite/Data/DataView.aspx).关键词：Wind Radar;scatterometer;instrument performance;in-orbit test;preliminary evaluation;radiometric resolution;data preprocessing;FY-3;meteorological satellite414|221|0更新时间：2024-10-18
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