最新刊期
卷 28 ， 期 10 ， 2024
- “最新研究进展显示，遥感技术已成为监测河流水文要素的主流方法，为理解河流变化过程提供新视角。”
摘要：Rivers are integral to the water cycle, underpinning human development, ecological health, and regional climate stability. Recently, global warming, glacial melt, and recurring hydrological disasters have intensified disturbances in river systems, necessitating broad-scale monitoring of complex hydrological changes. While traditional field measurements are valuable, limitations in their spatial and temporal coverage call for alternative approaches. With the advancement of sensor technology and the proliferation of satellite platforms, (satellite) remote sensing has emerged as a pivotal method for contemporary river hydrology monitoring. Compared with hydrological field measurements, it offers remarkable advantages in terms of real-time data acquisition, vast spatial coverage, and reduced economic costs. Various remote sensing monitoring techniques have been extensively applied to monitor river characteristics, such as area/width, water level fluctuations, runoff estimation, and forming diverse-scale remote sensing products of hydrological elements. This study reviews various monitoring techniques for river hydrological variables using optical or radar imaging and satellite altimetry. It analyzes the latest research progress in the hydrologic variables, encompassing river width, water area, water level, runoff, and their changes. Additionally, the spatial scale and feasibility of previous literature are thoroughly discussed. The Tibetan Plateau, known as the “Roof of the World,” is one of the regions with a serious shortage of in situ hydrological monitoring data, despite being the source of major rivers in Asia. The application of remote sensing technology for river hydrological monitoring on the Tibetan Plateau encounters challenges in data sharing, pronounced spatial and temporal heterogeneity of hydrological processes, and intricate response characteristics to a warming and humidification climate.This study begins by examining the main satellite remote sensing data sources and methods used to monitor various hydrological elements of rivers. It summarizes the current research progress in river hydrology monitoring using remote sensing technologies and explores future development opportunities. The review also addresses the advancements and challenges of hydrological remote sensing techniques specifically applied to river monitoring on the Tibetan Plateau. Several persistent issues in river hydrological remote sensing development have been identified: (1) The accuracy of extracting river area and width in regions with complex topography is severely affected by mixed pixels and spectral similarities. (2) In areas with sparse or no hydrological stations, assessing remote sensing data’s quality and potential applications remains challenging. (3) Comprehensive monitoring and studies on the spatial and temporal patterns of hydrological changes in the inland flow areas of the Tibetan Plateau are lacking. Future research directions for remote sensing of river hydrology are outlined as follows: (1) Multisource remote sensing data must be integrated, and the technologies and their applications must be enhanced for hydrological monitoring. (2) Universally applicable remote sensing algorithms for river hydrology must be optimized for innovation. These priorities aim to address the critical challenges in hydrological remote sensing and enhance the capability and accuracy of monitoring systems, particularly in complex and underserved regions, such as the Tibetan Plateau. This study aims to promote the deepening of river hydrology research on the Tibetan Plateau region, providing more accurate and scientific–technical support for practical water resource management and policy-making.关键词：River;Hydrology;remote sensing;Tibetan Plateau;water extent;water level;runoff- 497
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发布时间：2024-11-27 - “红树林碳库遥感研究进展，专家梳理了三个发展阶段，探讨了遥感技术融合分析结果改进程度，为红树林碳储量估算提供解决方案。”
摘要：Mangroves are important blue carbon ecosystems that play a key role in maintaining global marine carbon cycles and mitigating the rate of climate change. Remote sensing, due to its advantages of good repeatability, high resolution, and low cost, can better facilitate the monitoring and management of mangrove carbon resources. This study reviews the research progress of remote sensing-based mangrove carbon reservoirs and categorizes the development into three stages based on the research content and depth: the early exploration stage (2007—2012), which primarily focused on global mangrove mapping and the extraction of spatial structural information; the midterm application research stage (2013—2015), which estimated mangrove carbon stocks based on previous research achievements; and the comprehensive development stage (after 2016), characterized by improving accuracy in carbon stock estimation and a research focus on the impact mechanisms of environmental factors on mangrove carbon reservoirs. The current status of optical remote sensing and radar remote sensing methods is reviewed, and the degree of improvement in results through the fusion analysis of these two remote sensing techniques is explored. Furthermore, the performance of various mangrove carbon models in estimating carbon stocks and simulating carbon cycling in mangroves is discussed. From the two important carbon reservoirs of biomass and soil in mangroves, relevant research on their carbon stocks is reviewed. The biomass carbon reservoir is primarily composed of carbon stored in vegetation roots, stems, and leaves, and it is a major influencing factor in mangrove primary productivity. However, the biomass carbon stock is highly affected by human activities and natural influences, resulting in considerable fluctuations. The soil carbon reservoir, which accounts for approximately 49%—98% of the total carbon stock in mangroves, is the largest carbon reservoir in mangrove ecosystems. However, research on soil carbon reservoirs is relatively limited compared to biomass carbon reservoirs, primarily due to challenges in acquiring remote sensing data and dealing with complex optical characteristics. In consideration of the crucial role of mangrove ecosystems in carbon sequestration and the achievement of carbon-related goals, the need for improvements in applying mangrove carbon sinks to carbon accounting and statistics is analyzed, and the potential applications of unmanned aerial vehicle remote sensing technology and artificial intelligence in mangrove carbon stock estimation are explored.关键词：mangroves;remote sensing;Carbon model;Biomass carbon stock;Soil carbon stock;UAV remote sensing- 605
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发布时间：2024-11-27
Research Progress
- “在农业病害监测领域，专家利用多源遥感数据建立了水稻纹枯病生境适宜性评价模型，为病害预测提供参考。”
摘要：Crop diseases have a severe impact on food security, and the excessive use of pesticides in crop disease prevention and control is a common issue. The evaluation of disease habitat suitability can provide important information for disease forecasting and control. The occurrence of crop diseases is closely associated with factors, such as the growth status of host and environmental conditions, while disease habitat conditions vary considerably due to cultivation practices and microclimate variations in the field. At present, disease habitat monitoring and evaluation are generally coarse, mainly relying on meteorological information and lacking detailed descriptions of spatially heterogeneous factors, such as crop growth status and environmental conditions among fields. In this study, Rice Sheath Blight (RSB), a major disease widespread in rice cultivation, was selected as the research object; the disease surveys were conducted at a county level in 2018 and 2019. Multisource remote sensing data, including optical, microwave, and thermal infrared images, were used for monitoring the key disease habitat factors. Multitemporal Sentinel-2 optical images were utilized to extract the planting area of the host crop, which solved the problem of confusing the host with other vegetation in single phase images; the growth status of host was indicated by the tasseled cap products of Sentinel-2 optical images; the status of water layer in rice field was extracted by combining Sentinel-1 microwave images and Sentinel-2 optical images, the optical image of rice region was segmented by object-oriented analysis method to obtain the rice plot boundary to eliminate the noise of microwave image; and the MODIS land surface temperature products were utilized to reflect the evapotranspiration and respiration status of rice plants. On the basis of these remote sensing habitat features of the RSB and a spatial gridding analysis, the habitat suitability evaluation model was established using the partial least squares regression method.Validation results against the disease survey data showed that the remote sensing information can effectively characterize the disease habitat features. The R2 of the habitat suitability evaluation model was 0.60—0.65, and the RMSE was 0.72 and 0.56, respectively, and the output of the model was consistent with the actual spatial pattern of the disease. In addition, the hot and cold spots of the disease habitat suitability map were highly consistent with the actual pattern of disease occurrence in the region. Moreover, the rate of habitat suitability under each disease grade was analyzed, and the results further confirmed the rationality of the evaluation. Therefore, this study demonstrates the feasibility of utilizing multisource remote sensing data in evaluating the disease habitat suitability. The disease habitat evaluation map can be integrated into some disease epidemic models to develop spatiotemporal dynamic disease forecasting models at a regional scale, and multisource data, such as meteorological data, remote sensing data, and ground sensor networks, can be incorporated to establish a more comprehensive habitat suitability evaluation model, which is expected to be beneficial for large-scale disease control.关键词：rice sheath blight;habitat;remote sensing information;spatial gridding;evaluation model- 265
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发布时间：2024-11-27 - “最新研究揭示作物病害遥感监测机制，为精准防控和粮食安全提供新思路。”
摘要：Remote sensing monitoring of crop diseases plays a crucial role in food security in terms of the precision management of chemical fungicides and the efficient assessment of crop losses. Spectroscopic detection of disease infection has been investigated for numerous crop diseases individually. However, it remains unclear how biochemical and spectral variations differ in response to divergent diseases given the distinct symptoms caused by different pathogens. This study aimed to determine the pathological mechanism and specificity of the spectral responses of two types of fungal diseases by comparing their specific spectral signatures and disease monitoring performance. The biotrophic Wheat Powdery Mildew (WPM) and the semi-biotrophic Rice Leaf Blast (RLB) diseases were used as examples for the comparison. With the reflectance measurements of infected leaves and radiative transfer modeling, a comparative analysis for these two diseases was conducted in terms of spectral responses, leaf biochemical and structural parameters. Additionally, we assessed the specificity of various disease-related spectral features, which were proposed in previous studies for the monitoring of WPM or RLB, by accuracy comparison in the detection of diseased leaves and the estimation of Leaf Lesion Proportion (LLP). The results showed significant differences in the intensity of spectral responses to the two diseases despite the similarity observed in the general trend in spectral variations. In addition, distinct variations appeared in the spectral shape at the green peak and near-infrared plateau between WPM and RLB. Moreover, the pigment variations in response to two infections were generally similar, whereas the response was more pronounced for RLB. Notably, the leaf water content and structural parameter displayed significant changes only in relation to the severity of RLB. In disease detection, the spectral features developed for WPM or RLB generated higher accuracy in detection of the target disease than the other disease. Wavelet features of WF3820 and WF5866 displayed the highest accuracy and specificity for WPM and RLB, respectively. Regarding the severity quantification, most spectral features exhibited higher sensitivity to the LLP of RLB than to that of WPM. Specifically, a variat of rice blast index (RIBIred) and the Photochemical Reflectance Index (PRI) demonstrated the highest accuracy and specificity in the LLP estimation of WPM and RLB, respectively. Among the WPM- or RLB-related spectral features, RIBIred showed the optimal monitoring performance and specificity in both disease detection and severity estimation (Overall accuracy=0.74, R2=0.58). Our findings provide solid evidence and new insights into disease-specific spectroscopic monitoring by associating spectral responses with pathogenesis of two types of fungal diseases. This study offers significant contributions to the understanding of disease monitoring mechanisms and the identification of multiple diseases with hyperspectral remote sensing.关键词：remote sensing;Disease detection;severity quantification;PROSPECT;biochemical parameters;leaf structure;spectral index;continuous wavelet transform- 126
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发布时间：2024-11-27 -
Monitoring chilling damage of corn in the Northeast China based on MODIS LST and meteorological data AI导读
“东北地区玉米低温冷害遥感监测取得新进展，构建了日平均气温估算模型，为玉米产量预测提供科学依据。”
摘要：Corn has become one of the most important crops in China at present. The study area in this work, which includes Jilin Province, Liaoning Province, Heilongjiang Province, and the four eastern cities of Inner Mongolia Autonomous Region (Hulun Buir, Tongliao, Chifeng, and Xing’an League), is the most important corn production area. The area is also located at the northern limit of corn planting area. Notably, the temporal and spatial distribution of chilling damage is highly important to increase yield and quality. This study aims to integrate MODIS and meteorological data for monitoring corn chilling damage in Northeast China. The algorithm was computed in two steps. In the first step, the remote sensing estimation model of air temperature were established. In the second step, the sterile-type chilling damage and delayed-type chilling damage on corn were monitored based on the full coverage daily mean air temperature and the corn chilling damage indicator. Satellite data, including LST, EVI, and quality control data derived from TERRA/AQUA -MODIS, and ground-based data, including daily mean air temperature and phenological data observed by 234 meteorological stations, from 2003 to 2015 were collected for data analysis, image processing, and mapping.The remote sensing estimation model of air temperature was established by multi-variated linear regression using the MODIS LST, EVI, and solar declination of cloud-free pixels as independent variables, and daily mean air temperature observed by meteorological stations was used as a dependent variable. The meteorological stations were divided into two parts according to the coordinates. Daily mean air temperature measured by two thirds of station (156) from 2003 to 2013 was used to establish the daily average temperature estimation model, and the remaining data including the observations of 78 meteorological stations from 2003 to 2013 and the observation data of all stations in 2014 and 2015 were used to validate the model. The MODIS EVI production is the composited production on the 16th day. The S-G filter with max was used to achieve daily EVI. The air temperature of cloud-free pixels was calculated with estimated models using TERRA and AQUA daytime and nighttime data, respectively. Then, the TERRA and AQUA daytime and nighttime derived daily mean air temperature data were merged based on the R2 and RMSE to increase spatial cloud-free data. The validation results show that the models using TERRA or AQUA night LST data as predictors outperform those using daytime LST as predictors. A framework was proposed for the air temperature data fusion. The data fusion framework is based on the fact that the MODIS TERRA and AQUA can provide daytime and nighttime LST and that the merge of these data can increase spatial coverage. The daily mean air temperature dataset covering the whole study area with a spatial resolution of 1 km from 2003 to 2015 was completed based on the retrieval models and the data fusion framework.This study provided a remote sensing monitoring method of sterile-type chilling damage and delayed-type chilling damage on corn. Low daily mean air temperature and its last days are the corn sterile-type chilling damage indicator. Corn sterile-type chilling damage was identified by integrating the corn chilling damage indicator and the daily mean air temperature dataset. The results showed corn sterile-type chilling damage in 2003, 2006, and 2012. These findings are consistent with the meteorological observation. This research can be used to monitor the process of corn sterile-type chilling damage, take abatement measures to mitigate corn sterile-type chilling damage, and reduce disaster losses. On the basis of the full coverage daily mean air temperature dataset, the accumulated temperature of ≥10 ℃ from 2003 to 2015 was calculated. The indicators of delayed-type chilling damage on corn also revealed that the study area suffered from widespread delayed-type chilling damage in 2003, 2005, 2006, 2009, and 2011. Compared with the observation of meteorological stations, the results of this research match the actual situation.关键词：remote sensing;MODIS;land surface temperature;data fusion;chilling damage;corn- 780
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发布时间：2024-11-27 - “最新研究提出了基于时序谱标准曲线的大豆识别方法，通过气象因子变化分析地域差异和灾害影响，为大豆监测提供新思路。”
摘要：Soybean is the world’s most important legume crop that serves as a major source of high-protein food, the primary ingredient for livestock feed, and an essential source of edible oil. They play a crucial role in the world’s food production, with a global annual production of approximately 370 million metric tons. China is one of the major soybean-producing countries globally, with an annual production of approximately 17 million metric tons. However, China’s domestic soybean production is insufficient to meet production and living needs, and it is highly reliant on imports, accounting for more than 80% of its soybean consumption. Consequently, China’s food security faces considerable structural challenges. Remote sensing technology is a powerful tool for monitoring soybean cultivation and can provide basic data support for various countries to release signals of changes in agricultural product markets, strengthen the guidance of agricultural product markets, and formulate effective agricultural economic development strategies. The traditional method of estimating soybean planting area through agricultural surveys is usually time consuming, labor intensive, and subject to subjective factors, leading to inaccurate and imprecise results. By contrast, remote sensing technology utilizes satellite, aerial, or drone-based sensors to capture and analyze the electromagnetic radiation reflected or emitted by Earth’s surface, providing a more objective and efficient way of monitoring crop planting areas. While methods based on vegetation index time series and phenology are widely used for crop recognition including soybeans, the focus has been primarily on the impact of the vegetation index time series feature or phenological feature on soybean recognition, and research on the time series curve itself has been limited. Furthermore, analysis and research on the spectral characteristics of the key growth stages for soybeans are lacking, and no standard spectral time series curve for soybeans has been established to summarize their changing patterns. Additionally, mainstream crop recognition methods face difficulties in obtaining samples, especially in large-scale mapping, where the quality and quantity of samples are the main limiting factors.This study proposes a soybean recognition method based on the standard spectral time-series curve on the Google Earth Engine (GEE) cloud platform. By adding the climate weight factor, the standard spectral time-series curve of soybean can be accurately recognized.By using the features of the standard spectral time-series curve combined with the random forest classifier, a soybean distribution map in Heilongjiang Province in 2020 is extracted. The classification confusion matrix shows that the overall accuracy of soybean recognition is 86.95%, the user accuracy is 90.91%, the producer accuracy is 86.14%, and the F1-score is 0.8846. Compared with statistical area data, the area accuracy reaches 95.94%.The study focuses on analyzing the difference between the spectral time-series curves of soybean and corn and the impact of meteorological factors on the curves and establishes a method for mapping the standard spectral time-series curve information to samples, thereby solving the problem of insufficient samples for mapping a large area of soybean. Furthermore, this study designs experiments to verify the robustness of this soybean recognition method based on the standard spectral time-series curve in terms of time scale and disaster situations. These results provide scientific evidence and technical support for the monitoring of soybean growth, disaster evaluation, and the formulation of international agricultural product trade strategies.关键词：remote sensing;soybean;Temporal spectrum;Standard curve;phenology;Meteorology- 767
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发布时间：2024-11-27
Remote Sensing of Vegetation Pests and Diseases
- “最新研究揭示，P波段SAR数据在估测寒温带针叶林蓄积量方面表现最佳，联合L或S波段可进一步提升估测精度。”
摘要：Research investigating the estimation ability of forest stock volume combining multiband polarimetric SAR (PolSAR) has hardly been explored, particularly the complementarity between long wavelengths, such as P-band and other shorter wavelengths. This study takes cold temperate coniferous forests in Inner Mongolia as the research object. Having available a multiband stack of airborne P-, L-, S-, C-, and X-band PolSAR data acquired by the high-resolution airborne multidimensional space joint-observation SAR (MSJosSAR) system, the aim is to analyze systematically the response and sensitivity of polarimetric characteristics in different bands to forest stock and evaluate the performance of forest stock retrieval using single and multiband PolSAR data.First, geocoding and terrain radiometric correction were performed on multiband PolSAR data, and then a polarimetric feature set containing backscatter intensity and polarization decomposition components was extracted. Second, on the basis of the water cloud model and correlation coefficient, the response law and sensitivity of polarimetric characteristics in different bands to forest stock was analyzed. Finally, machine learning algorithms were used to perform feature selection and modeling, and the ability of each band and jointly with multiband to estimate forest stock was evaluated.The response of backscatter intensity in different bands to forest stock shows a similar upward trend, but the saturation point varies depending on wavelength and polarimetric channel. Among them, the saturation point for the P-band is higher than 160 m3/ha, whereas it does not exceed 110 m3/ha for the other bands. In addition, the correlation between forest stock and the P-band, L/S-band, and C/X-band decreases in order, with values above 0.6, between 0.3 and 0.4, and below 0.3, respectively. When forest stock was estimated on the basis of a single band, the accuracy of the P-band was 73.79%, and the accuracy of other bands did not exceed 60%. When multiband joint estimation was used, the estimation accuracy of L- or S-band and P-band joint estimation was approximately 2% higher than using P-band alone. The contribution of adding the C- or X-band to the accuracy improvement was minimal. The best estimation performance was achieved through the combination of all bands with an accuracy of 77.25%.Considering various indicators, such as signal dynamic range, saturation point, and correlation, the P-band exhibits the highest sensitivity to forest stock, followed by the L/S-band, and the C/X-band, which is the least sensitive. Therefore, when estimating forest stock using PolSAR data, the P-band should be the first choice. Additionally, when using multiband joint estimation, the combination of P- and L- or S-band should be preferred. In recent years, long-wavelength SAR satellites are being vigorously developed from China and overseas, e.g., China’s LT-1 satellite is already in orbit, ESA BIOMASS and NASA-ISRO NISAR missions are about to be launched, and China’s civil P-band SAR satellite has also entered the preliminary research stage. The above long-wavelength SAR satellites will greatly enhance the estimation ability of regional forest stock in our country and provide strong support for the refined and scientific management of forest resources.关键词：remote sensing;multi-band SAR;polarimetric SAR;saturation point;forest stock;water cloud model- 762
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发布时间：2024-11-27 - “最新研究突破了植被覆盖区地形提取难题，利用TanDEM-X和Sentinel-2数据，显著提升了林下地形精度，为大范围地形获取提供新方案。”
摘要：The Digital Elevation Model (DEM) is one of the most important data sources for various scientific studies and applications. Currently, one important data source for large-scale DEM generation originates from the TerraSAR-X add-on for digital elevation measurement (TanDEM-X) mission, which provides bistatic interferometric Synthetic Aperture Radar (InSAR) data with high spatial resolution (12 m) at the global scale. However, in forest areas, the retrieval of the subcanopy topography using TanDEM-X InSAR data still faces notable challenges because of the effects of the forest scattering process on InSAR height measurements and the limited penetration capability of X-band’s signals, causing the measured elevation to be between the ground surface and the top of the tree canopy. Although SAR signals with long wavelength has strong penetrability in the forest layer, subcanopy topography still cannot be measured due to the volume scattering effect from tree canopies or trunks. In addition, the missing space-borne PolInSAR or TomoSAR data pose another limitation for subcanopy topography estimation. In this study, a new method to extract subcanopy topography over forested areas is proposed. The method uses a combination of TanDEM-X DEM and Sentinel-2 multispectral data. TanDEM-X DEM and the multiband data of Sentinel-2 are regarded as the input variables, while the high-precision ground elevation data was considered as the target variable. Subsequently, the random forest fitting method is used to construct the subcanopy topography estimation predictive model. According to the obtained model, we can extract a large-scale subcanopy topography over the areas without reference data. Results show that the subcanopy topography derived via the proposed method has an RMSE of 3.7 and 7.78 m for the two forest sites, representing an improvement of approximately 76% and 63%, respectively, in comparison with the original TanDEM-X DEM. The experimental results also show that the resultant subcanopy topography can maintain more detailed topographic information. All these findings indicate that based on publicly available data, the proposed method has great potential for extracting large-scale subcanopy topography at high spatial resolutions.关键词：remote sensing;TanDEM-X;Sentinel-2;machine learning;Digital Elevation Model (DEM);sub-canopy topography- 394
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发布时间：2024-11-27 - “最新研究显示，利用高分六号宽幅相机数据，通过叶绿素含量敏感指数CSI反演叶片叶绿素含量，精度优于其他红边指数，为农业和植被生态监测提供数据支撑。”
摘要：Chlorophyll is the dominant pigment in plant photosynthesis. Leaf chlorophyll content (ChlLeaf) is directly related to the photosynthetic capacity and plays an important role in global carbon cycle modeling and agricultural monitoring. GF-6 satellite is China’s first high-spatial-resolution satellite for precision agriculture. The GF-6 Wide Field of View (WFV) camera with a 4-day revisit cycle and 16-meter spatial resolution has two red-edge bands that are sensitive to variations in ChlLeaf and shows great potential for ChlLeaf monitoring at fine temporal-spatial resolution. However, a few studies focusing on vegetation parameter quantitative inversion from GF-6 WFV data and the applicability of GF-6 WFV for ChlLeaf retrieval have yet to be validated.In this study, we proposed a ChlLeaf retrieval algorithm for GF-6 WFV based on Chlorophyll Sensitive Index (CSI) and constructed a CSI-based empirical regression model using the relationship between ChlLeaf and CSI using PROSAIL and PROSPECT+4-scale model simulations. The inversion accuracy of the CSI-based empirical regression model was then compared with other vegetation index-based empirical regression models, such as MTCI, CIre, TCARI/OSAVI. First, the PROSAIL and PROSPECT+4-scale models were used to generate simulated the canopy reflectance of croplands, broadleaf forests, and needleleaf forests, and the canopy reflectance simulations were resampled to GF-6 WFV multispectral reflectance using the spectral response function of GF-6 WFV. Then, CSI derived from simulated GF-6 WFV reflectance was used to construct the CSI-based empirical model for ChlLeaf retrieval via regression analysis. Finally, the accuracy of the CSI-based retrieval model was evaluated using ground-measured ChlLeaf data and the existing MODIS ChlLeaf product.Results showed that CSI was more linearly related to ChlLeaf and less sensitive to LAI variations than MTCI, CIre, and TCARI/OSAVI. CSI achieved improved ChlLeaf retrieval accuracy with R2=0.62 and RMSE=10.31 μg cm-2, higher than CIre (R2=0.34, RMSE=14.83 μg cm-2), MTCI (R2=0.25, RMSE=15.3 μg cm-2), TCARI/OSAVI (R2=0.01 and RMSE=21.34 μg cm-2). Under different LAI and ChlLeaf conditions, the variations of the CSI-based model in RMSE are the lowest, suggesting that CSI offered a more stable approach to retrieving ChlLeaf compared with the other three vegetation indices. A comparison of the GF-6 WFV ChlLeaf time series and the MODIS ChlLeaf product at the Beijing forest site indicated that GF-6 WFV could provide a high spatial resolution ChlLeaf dataset, which can derive information on ChlLeaf variations at a fine temporal-spatial resolution.In conclusion, the GF-6 WFV data have good potential for the accurate retrieval of ChlLeaf at regional scales. The CSI-based GF-6 ChlLeaf can achieve high retrieval accuracy and portray the spatial and time-series variation characteristics of ChlLeaf, which provide the data support and scientific basis for the further research and application of GF-6 WFV in the ecological monitoring of agriculture and vegetation.关键词：remote sensing;leaf chlorophyll content;GF-6;chlorophyll sensitive index;PROSAIL;PROSPECT+4-Scale- 95
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发布时间：2024-11-27 - “卫星遥感物候监测研究取得新进展，专家评估了地面相机观测角度对验证结果的影响，为提高监测可靠性提供依据。”
摘要：In the fields of agricultural production management and climate change research, monitoring large-scale plant phenology with satellite-based remote sensing is crucial to reveal the interaction of biology and nature environment. During validation on remotely sensed phenology information, near-surface digital cameras are often employed as main data sources. However, more efforts were focused on the scale difference between ground and remotely sensed data and rarely on the difference of sensors viewing zenith, i.e., the satellites mainly adopted the near-nadir observation while cameras were mostly inclined in arrangement. Vertical (PhotoNet) and inclined (PhenoCam) camera observations were acquired at the similar latitude for the same vegetation type, and then these observations were compared with phenological parameters extracted from Sentinel-2 data to assess systematically the effects of camera observation angles on the results of satellite phenological verification. For 16 locations, we compared a Greenness Chromatic Coordinate (GCC) series derived from digital cameras and Sentinel-2. A double hyperbolic tangent model was fitted for each series. The threshold method was applied to the annual complete modeled data, and the curvature extremum method was used for incomplete data to estimate the onset of greenup, the maturity of the green canopy, the peak of season, the end of greenness, and the dormancy of the green vegetation (OG/MG/PS90/EG/DG). Results showed that the viewing zenith of cameras is one of the uncertain sources to validate phenology information from satellite imagery. In most cases, the vertically observed camera showed improved agreement with the phenological parameters extracted by the satellite-based method, with an average bias of 9 days, while a larger bias of 19 days was observed for inclined camera observation. Therefore, the two camera observation methods result in the verification deviations of up to 10 days on average. However, the deviations might be vegetation type and growth stage dependently. The bias of vertical observation was remarkably higher than that of inclined observation during the end to dormant period for maize. The different results of the vertical and inclined cameras on the similar vegetation can be partly explained from the directional reflection characteristics of vegetation canopy and the difference of target components (e.g., different fractions of soil and vegetation, photosynthetic and nonphotosynthetic components of vegetation) within the camera field of view. Results demonstrate that the viewing zenith angle of the near-surface cameras is an important factor in the validation of satellite phenological parameters. In addition, the uncertainty of verification results caused by the phenological period extraction method, illumination, and satellite observation geometry is limited, which is not the main factor affecting the verification of satellite phenology parameters. As a result, the verification error introduced by the angle effect should be fully considered while near surface cameras are deployed in the field to provide more reliable verification data for satellite remote sensing monitoring.关键词：remote sensing;digital camera;observation angle;remotely sensed phenology;near-surface phenology;vegetation greenness- 332
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发布时间：2024-11-27
Forestry and Agriculture
- “最新研究揭示了沙漠地区沙尘气溶胶散射效应对卫星CO2反演精度的影响，为提高反演算法精度提供参考。”
摘要：Carbon dioxide (CO2) is an important greenhouse gas. Satellite remote sensing of atmospheric CO2 has the advantages of long-term and wide spatial range observation, which is crucial for verifying emission reduction strategies to cope with global warming. Aerosol scattering in the atmosphere is considered a major obstacle for remote sensing retrieval of CO2 with high accuracy. Previous studies have shown that over areas with high surface albedo, such as desert regions, satellite retrievals of atmospheric column-average dry-air mole fraction of CO2 (XCO2) are systematically overestimated, and the bias can reach 50% of the allowable error to meet the practical application requirements. However, sufficient understandings and quantitative analysis of the systematic bias are still lacking. Focusing on this difficult problem, this thesis analyzes and quantifies the bias of XCO2 retrievals caused by the scattering effect of dust aerosol over desert regions using an accurate atmospheric radiative transfer model and a retrieval algorithm based on optimal estimation. This study starts from three important representative variables of aerosols, including aerosol optical depth (AOD), aerosol layer height (ALH), and single scattering albedo (SSA), to illustrate the physical mechanism of dust aerosol scattering effects on XCO2 remote sensing retrievals. From the perspective of spectral radiance generated from forward radiative transfer model, increasing AOD leads to a decrease in the spectrum continuum level (defined as radiance of channels where gas absorption can be neglected) in the case of high surface albedo through its extinction effect. Increasing ALH causes reduced relative absorption depth (defined as the ratio of radiance difference between continuum level and absorption channels to continuum level), which is closely related to the XCO2 retrievals. From the perspective of retrieval model, this thesis conducts separate retrieval experiments using the O2 A band and the WCO2 band, respectively, and joint retrieval experiment using both bands. Results show that the underestimation of AOD or ALH of dust aerosols or the overestimation of SSA in satellite retrieval algorithms can be possible causes of the overestimation of XCO2 over deserts. Specifically, (1) in the case of not considering aerosol in the retrieval algorithm, XCO2 retrievals are overestimated by more than 1% when the actual AOD is larger than 1.0; (2) when AOD is underestimated by a value between 0.3 and 0.5, XCO2 retrievals are overestimated by 0.15%—1.28%; (3) when ALH is underestimated by more than 0.6 km, XCO2 retrievals are overestimated by more than 1%; (4) when SSA is overestimated, XCO2 retrievals are also overestimated but by no more than 0.15%. These simulation experiments reveal that accurate aerosol information is crucial to achieving accurate atmospheric XCO2 retrievals. Additionally, this thesis discusses the impact of potential “critical albedo” on retrievals and demonstrates that its effect is probably the cause of the bias in extracting useful aerosol information from CO2 monitoring satellites. This thesis proposes that this difficult problem can be addressed when observations from aerosol-observing instruments are included in actual retrievals to further constrain the aerosol information to improve the accuracy of XCO2 retrievals.关键词：dust aerosol;carbon dioxide satellite;Remote Sensing Retrieval Algorithm;Scattering Effect;radiative transfer model- 187
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发布时间：2024-11-27 - “风云三号D星和E星搭载的MERSI可用于大气顶射出长波辐射反演，两颗星反演精度基本一致，利用两颗卫星观测数据可更精确反映日变化特征。”
摘要：The Outgoing Longwave Radiation (OLR) of the top of the atmosphere is an important component of radiation energy balance. The FY-3D and FY-3E polar-orbiting meteorological satellites, launched in November 2017 and July 2021 respectively, carry the medium resolution spectral imager (MERSI) II and low light instruments. Both instruments can retrieve OLR using two water vapor channels and two window channels. In this study, on the basis of the introduction of the MERSI OLR inversion algorithm of the Fengyun satellite, the instantaneous OLR retrieval accuracy of FY-3D and FY-3E MERSI is compared by using the instantaneous observation data of Aqua CERES OLR. Comparison results show that the instantaneous OLR retrieval accuracy of FY-3D and FY-3E is basically the same as the instantaneous OLR data of Aqua CERES. The Root Mean Square Error (RMSE) of FY-3D and FY-3E MERSI OLR is between 6 and 7 W/m2 compared with that of Aqua CERES OLR. This result reflects that although the performance of the MERSI instruments of the FY-3D and FY-3E satellites have differences, the OLR retrieval capabilities of the two satellites are comparable. The comparison results of the daily average OLR data based on CERES between the single and joint calculations of FY-3D and FY-3E show that the daily average OLR calculated on the basis of the four times of the two satellites per day is 3—4 W/m2 higher than that calculated twice a day for a single satellite. The global daily average OLR data from June to December 2022 were selected as an example. Compared with the daily average OLR of CERES, the daily average OLR obtained by two FY-3 satellites is remarkably improved in comparison with the RMSE of the daily average OLR calculated by a single satellite. This result shows that the daily observation data of multiple polar-orbiting meteorological satellites can reflect the diurnal variation characteristics of OLR well. The comparison involves the spatiotemporal matching of data, reprojection, and resampling schemes affects the validation results. Compared with CERES, the difference between the retrieval OLR of the low-temperature target is higher than that of the high-temperature target regardless of the daily average OLR calculated on the basis of the single or double satellites of FY-3. The reasons for this are multifaceted and must be further analyzed. Research results show that the joint application of multiple polar-orbiting satellites with a certain instantaneous observation interval can effectively improve the calculation accuracy of the daily average OLR in the cloud area. However, how to construct the daily variation model of OLR and deepen the calculation method of the daily average OLR must be further studied.- 102
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发布时间：2024-11-27 - “海洋二号D卫星DORIS系统定轨精度研究取得进展，专家构建卫星姿态模型，评估轨道精度，为提高卫星定轨精度提供解决方案。”
摘要：The Haiyang 2D satellite (HY-2D) is the fourth marine dynamic environment satellite in China, which belongs to the marine remote sensing satellite series in China, with high-precision orbit measurement, orbit determination capabilities, and all-weather, all-day, global detection capabilities. The main mission of the satellite is to monitor and investigate the marine environment; obtain various marine dynamic environmental parameters, including sea surface wind field, wave height, and sea surface height; directly provide measured data for the early warning and prediction of catastrophic sea conditions; and provide support services for marine disaster prevention and reduction, marine rights and interest protection, marine resource development, marine environmental protection, marine scientific research, and national defense construction. After the launch of the HY-2D satellite, the HY-2B and HY-2C satellites realize three-star network observation. An accurate orbit of HY-2D is a prerequisite for mission accomplishment. It is equipped with the DORIS receiver for precise orbit determination. The attitude of the satellite alternates between fixed mode and nominal yaw-steering mode. To study the accuracy of orbit determination of HY-2D using DORIS, we build a satellite attitude model, select DORIS phase measurement data from June 5 to June 13, 2021, and adopt the epoch-difference and dynamic orbit determination methods for precise orbit determination. Orbital accuracy is evaluated by postfit residuals and by overlapping and comparing with CNES and SLR. The differences between the two orbit determination results using attitude model and attitude data are discussed. Results show the following: (1) The mean of the postfit residuals is 0.355 mm/s, and the mean of 3D RMS is within 2 cm. (2) Compared with the precise orbit by CNES, the RMS values when using attitude data in the R, T, and N directions are 1.02, 2.92, and 3.11 cm, respectively, and the RMS values when using the attitude model in the R, T, and N directions are 0.97, 2.77, and 3.15 cm, respectively. The similarity between the two results indicates that the constructed satellite attitude model is consistent with the measured attitude data. (3) The mean RMS of SLR residuals for the CNES orbit and our orbit are 2.38 and 2.24 cm, respectively, indicating that the two orbits have similar accuracy. Our research shows that the on-board DORIS receiver has good and stable performance. By using the received data, we can provide a centimeter-level HY-2D satellite orbit, which can ensure the stable operation of satellite altimetry.关键词：HY-2D satellite;DORIS;precise orbit determination;satellite attitude;SLR- 209
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发布时间：2024-11-27
Atmosphere and Ocean
- “据最新报道，一项研究揭示了阿富汗6.0级地震前后微波亮温异常现象，为地震遥感观测提供新视角。”
摘要：The Arabian plate continues to squeeze the Eurasian plate northward, which promotes stress field changes, local stress locking, and rupture instability. This phenomenon resulted in the magnitude 6.0 shallow earthquake on June 21, 2022 in Paktita province on the Afghanistan-Pakistan border, which is the largest earthquake in the region in the past 10 years. Studying the phenomenon and mechanism of earthquakes is crucial. By analyzing an earthquake case with multisource data, the eternal earth science topic of earthquake perception and cognition is studied deeply. In this study, the Microwave Brightness Temperature (MBT) data collected by the AMSR-2 radiometer of the Aqua satellite was used to extract pre-earthquake and postearthquake MBT residuals within more than a million square kilometers around the epicenter by using a spatiotemporally weighted two-step method, revealing the spatiotemporal evolution characteristics of MBT and the polymorphism of positive MBT anomalies. On the basis of the data of precipitation, soil moisture, regional geological map, land cover, and greenhouse gases, such as CH4 and CO, the attribution analysis of positive polymorphic MBT anomalies was discriminated one by one. We found that (1) the positive MBT anomaly in the Indus Plain, southeast of the epicenter, and the positive MBT anomaly in the Karakum desert, northwest of the epicenter, could be attributed to the cavity particles (P-hole) activated by the seismogenic stress, transferring from the seismogenic area to the Quaternary overburden along the stress gradient, which reduces the dielectric constant of the dielectric constant in shallow surface layer; (2) the positive MBT anomaly in the alpine area during the earthquake period could be attributed to the transfer and accumulation of stress-activated P-hole to the alpine low-temperature area, which resulted in the decrease in the microwave dielectric constant of the sandy layer; (3) the positive MBT anomaly along the Herat Fault, northwest of the epicenter, was related to the fault stretching during the imminent earthquake and might have been affected by the greenhouse effect caused by the degassing of coal-bearing formations along fault and coal mines. In this study, the temporal and spatial evolution of the MBT of the Paktika earthquake was analyzed. Results showed that the MBT positive anomalies was affected by many factors, such as regional plate structure activity, geological lithology, and surface land cover. The MBT anomaly in seismogenic stage was polymorphic and must be carefully screened using multisource information and multiparameters. This study is crucial for observing and identifying the seismic anomaly in West Asia and has reference value for seismic remote sensing monitoring and anomaly recognition in other parts of the world.关键词：remote sensing;microwave brightness temperature;Seismic anomaly;P-hole;microwave dielectric;greenhouse effect;crustal stress field alteration- 91
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发布时间：2024-11-27 -
摘要：With the availability of remote sensing images since the 1970s, the spatial-temporal continuum observations of the land surface can be obtained at the global scale. In this manner, remote sensing is an important information source for the large-scale estimation of land surface carbon, water, and energy fluxes. Global eddy covariance flux datasets are widely used to evaluate and produce remote sensing flux products. Given that tower-based fluxes can only represent the small areas around the tower, a mismatch usually occurs between the tower-based fluxes and multiscale pixels of remote sensing. Thus, the spatial representativeness of flux footprints must be evaluated at multiscale pixels.In this study, we choose the Wanglang Mountain Remote Sensing Field Observation and Research Station of Sichuan Province, a typical mountainous ecosystem of Southwest China, as the study area. This study used a two-dimensional parametric footprint model (flux footprint prediction, FFP) to characterize the spatiotemporal variations and analyze the spatial representativeness of flux footprints at multiscale pixels (i.e., 30, 60, 120, 250, 500, 1000, 1500, and 2000 m). In this work, the land cover types and normalized difference vegetation index were used to characterize the spatial representativeness of footprint among vegetation types and vegetation density at multiscale pixels, respectively. At the same time, two site-level simple representativeness indices for land cover type and vegetation density were proposed to evaluate the footprint-to-pixel representativeness across flux towers at Wanglang station.Results showed that the footprint fetch varied across flux towers at Wanglang station (10—103 m), and the footprints at multiple temporal resolutions had a low symmetry (usually less than 40%). For the temporal variations of footprints, the overlap of footprints had evident changes at the daily scale (0%—88%), and the variations were reduced at the monthly scale (usually larger than 83%). As for the three flux towers around Wanglang station, results showed that the station of deciduous broadleaf shrub (with observed height at 10 m), deciduous broadleaf forest (with observed height at 30 m), and evergreen needleleaf forest (with observed height at 75 m) had the optimal spatial representativeness at the pixel scales of 30, 60, and 1000 m, respectively. Moreover, compared with vegetation density, the discrepancies of spatial representativeness were more evident for vegetation cover. The spatial representativeness differences of footprints must be paid attention to while validating remote sensing models and producing flux datasets around mountainous ecosystems. Moreover, the corresponding footprints must be combined with tower-based observations to characterize the temporal variations of fluxes when modeling and producing flux products at high temporal resolution (e.g., daily scale). Given that the high spatial representativeness of footprints was limited to the pixels at high (a lower tower) and medium-low (a higher tower) spatial resolution, the estimation of ecosystem parameters and flux research over mountainous areas could be promoted by cognizing the spatial representativeness of footprint at pixel scales and combining the multiscale remote sensing observations with the spatial and temporal scaling method.关键词：pixel;spatial representativeness;eddy covariance;flux footprint model;mountainous ecosystem- 172
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发布时间：2024-11-27 - “在电力巡检领域，专家提出了基于多特征约束的杆塔点云自动提取方法，为长距离规模化应用提供解决方案。”
摘要：Pylons are an important component of the transmission line, and its identification based on airborne Light Detection and Ranging (LiDAR) is crucial to power inspection. The efficient and high-precision extraction of pylon point clouds is important, especially in long-distance and large-scale applications, and is also conducive to massive data organization, parallel processing, and quantitative applications. The existing pylon extraction methods usually require a balanced and tremendous amount of training samples or lack sufficient terrain adaptability. Furthermore, these methods are vulnerable to tall objects, such as trees and buildings in the complex terrain environment of the mountainous areas. This study proposes an automatic pylon extraction method based on multifeature constraints. First, the height above the ground and the maximum vertical gap are designed on the basis of the spatial distribution of objects in the transmission corridor point clouds. Second, a series of preprocessing tasks, such as denoising and filtering, is performed on airborne LiDAR point clouds. Third, the pylon regions are quickly located on the basis of multifeature constraints, such as height difference and linearity, and the pylon center coordinates are calculated by using the layered density method and pylon structural symmetry. Finally, the point clouds of pylon regions are vertically sliced along the Z axis, and the nonpylon point clouds are eliminated layer by layer using the gap between the interference and the pylon vertical slicing. Airborne LiDAR point clouds in three different scenarios are utilized to evaluate the performance of the proposed method. The root mean square error of the pylon center coordinates are 0.04, 0.40, and 0.13 m. The precision, recall, and F1-value of the pylon extraction can reach up to 91.6%, 96.0%, and 93.5%. Compared with other pylon extraction methods, the qualitative analysis results show that the proposed method performs better in pylon area recognition, positioning error, and pylon point cloud extraction. Meanwhile, the proposed method successfully extracts pylons from variable terrain point clouds. Experimental results show that the proposed method can effectively extract pylons with high accuracy and strong terrain adaptability. In addition, the method does not need to train samples and consider class-imbalance problems. Furthermore, the proposed method can provide auxiliary information for postprocessing, such as scene classification and line hanging point extraction, and promote the application of airborne LiDAR for power inspection.关键词：Airborne LiDAR;multifeature constraint;Transmission corridor;Vertical slicing;Automatic extraction of pylons- 579
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发布时间：2024-11-27 - “在遥感图像场景分类领域，专家设计了ADC-CPANet模型，通过局部和全局特征提取，实现了96.43%的高分类准确率。”
摘要：The rapid development of remote sensing technologies, such as satellites and unmanned aerial vehicles, has led to a surge in the amount and types of high-resolution remote sensing images. This advancement marks the onset of the “era of remote sensing big data.” Compared with low-resolution ones, high-resolution remote sensing images provide richer texture, detailed information, and a more complex structure, making them crucial for applications like urban planning. However, images within the same category can vary substantially, whereas images from different categories may appear similar. Therefore, multi-scale feature extraction is important for remote sensing image scene classification. Current methods for remote sensing image scene classification can be divided into two categories according to the feature representation: those based on manual design features and those based on deep learning. Those based manual design features cover scale-invariant feature transformation and gradient scale histogram. They can achieve good results for simple classification tasks, but the feature information they extract may be incomplete or redundant, so the accuracy of classification in complex scenes remains low. By contrast, the methods based on deep learning have made incredible progress in scene classification owing to their powerful feature extraction ability. Compared with traditional methods, Convolution Neural Networks (CNNs) are commonly used in visual tasks, particularly those that involve more complex connections and diverse convolution forms. CNNs are effective at extracting local features, but they struggle with capturing long-distance dependencies among features. The Transformer architecture, which has recently been applied to computer vision, addresses this limitation through its self-attention layer that enables global feature extraction. Recent studies show that hybrid architectures combining CNNs and Transformers can utilize their advantages. This study proposes an Aggregation Depth-wise Convolution (ADC) module and a Convolution Parallel Attention (CPA) module. The ADC module effectively extracts local feature information and enhances the robustness of the model to image flipping and rotation. The CPA module integrates global and local feature extraction, with a multi-group convolution head decomposition designed to expand the receptive field and enhance feature extraction capacity. A remote sensing image scene classification model called ADC-CPANet is designed on the basis of two modules. The ADC and CPA modules are stacked at each stage of the model, improving its ability to extract global and local features. The effectiveness of ADC-CPANet is validated using the RSSCN7 and Google Image datasets. Experimental results demonstrate that ADC-CPANet achieves classification accuracies of 96.43% on the RSSCN7 dataset and 96.04% on the Google Image dataset, outperforming other advanced models. ADC-CPANet excels in extracting global and local features, achieving competitive scene classification accuracy.关键词：remote sensing image;scene classification;convolutional neural network;Transformer;Multi-Gconv Head Decomposition Attention;ADC-CPANet model- 1110
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发布时间：2024-11-27 -
Combining multisource satellite data to estimate lake basin bathymetry for seasonal water bodies AI导读
“记者从谷歌地图引擎GEE云平台获悉，基于激光雷达和光学传感器数据，提出了一种面向季节性水体的湖盆地形定量估算方法，为全球范围内季节性水体水深数据获取提供新思路。”
摘要：Seasonal water bodies are an important component of global surface water and play an indispensable role in regional flood storage and local biodiversity maintenance. Obtaining high-precision bathymetric information is key to supporting effectively the estimation of water storage and carbon flux in seasonal water bodies, which is helpful in understanding the regional hydrological processes and material-energy balance and other issues. On the one hand, only a few existing studies have focused on seasonal water bodies because of their complex subsurface conditions, particularly small water bodies, which make it even harder to evaluate bathymetry. On the other hand, using traditional bathymetric techniques may encounter great difficulties, where a single sensor cannot balance cost, efficiency, and accuracy. To this end, this study proposes a quantitative estimation method of underwater topography for seasonal water bodies, combined with active LiDAR and passive optical sensor data. The LiDAR data are obtained from ICESat-2/ATLAS global geolocated photon data (ATL03) product, which provides high-precision photons’ vertical profile information of the lake basin. Meanwhile, optical sensor data can be derived from Sentinel-2 MSI datasets based on the Google Earth Engine (GEE) cloud platform, where massive commonly used datasets can be accessed, and then the regional Inundation Frequency (IF) distribution is generated. Each photon along ICESat-2 ground tracks is time tagged and geolocated; thus, each photon’s height (Hgt) and IF value can be obtained by geographical intersection. In the same lake basin, every point’s height and inundation frequency are correlated in theory; thus, we can build an “Hgt-IF” model to fit this correlation. Then, this model is applied so that regional IF distribution can be translated into lake floor elevation over the lake basin. As a typical seasonal lake composed of some dished lakes, Poyang Lake is taken as the research object in this study, and systematical evaluation is performed to evaluate the estimation accuracy and applicability of the method. Results show that the quantitative estimation method is feasible based on the photon elevation of ICESat-2/ATLAS profiles and the inundation frequency information obtained from Sentinel-2 MSI to achieve the “point-to-surface” topography of seasonal water bodies. As for most dished lakes selected, the R2 values between the predicted and measured yield are greater than 0.7, and the root mean square errors are controlled within 1.0 m. However, the simulation accuracy of dished lakes in different areas also varies due to the combined effects of various factors, such as lake area, subsurface conditions, inundation frequency range, and photon track distribution. In summary, the proposed method can realize the quantitative estimation of underwater topography for seasonal water bodies in general terms. Combining active and passive remote sensing data can make up for the shortcomings of a single sensor, especially when it comes to a large-scale, low-cost, and long time-series situation. This method is also expected to provide ideas and directions for the development of bathymetric retrieval models for seasonal water bodies at the global scale.关键词：seasonal water bodies;lake bathymetry;satellite-based bathymetry;ICESat-2;Sentinel-2;inundation frequency- 282
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发布时间：2024-11-27
Models and Methods
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