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Speckle Anisotropic Diffusion Suppression by Multidirectional Sobel
Jing ZHANG,Fengcheng GUO,Zedan ZUO,Pengchen DING,Siguo CHEN,Chuang SUN,Wensong LIU
Remote Sensing Technology and Application    2023, 38 (5): 1118-1125.   DOI: 10.11873/j.issn.1004-0323.2023.5.1118
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Speckle is an inherent property of SAR image, but its existence seriously interferes with the quality of SAR image and affects the high-quality application based on SAR image, so it is urgent to suppress it. The accuracy of the edge detection model of the traditional AD (Anisotropic Diffusion) filter still has room for improvement, and the noise suppression effect is often limited by the problem that it is difficult to accurately estimate the diffusion threshold. To solve the above problems, a novel AD filter based on Multidirectional Sobel (MSAD) is proposed. MSAD filter is an improved algorithm of SRAD. It builds a new edge detection model based on Multidirectional Sobel templates. Based on this, a new AD diffusion coefficient is established by integrating Gaussian kernel function, which can effectively solve the limitation of traditional AD diffusion coefficient by parameter estimation and improve the accuracy of speckle anisotropy suppression. Three real SAR images are selected for filtering experiments. In experiments, SRAD, DPAD, EnLee, and PPB filters are selected as the comparison algorithms; ENL, SSI, ESI, and M-Index are selected to evaluate the performance of proposed algorithms. Experiments show that MSAD filter can effectively improve the edge detection ability and obtain better speckle suppression effect.

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Research Progress of Remote Sensing Image Analysis and Application Oriented to GEE Platform
Zhongliang HUANG,Jing HE,Gang LIU,Zheng LI
Remote Sensing Technology and Application    2023, 38 (3): 527-534.   DOI: 10.11873/j.issn.1004-0323.2023.3.0527
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Google Earth Engine (GEE) is a comprehensive application platform that integrates remote sensing image storage and analysis. It can conveniently and quickly call remote sensing images and information extraction. Therefore, GEE has attracted more and more scientific researchers' attention. With the continuous expansion and upgrade of GEE, the system platform has become more and more complex. For ordinary users, it is becoming more and more difficult to quickly understand its architecture and functional algorithms. In response to this problem, this article systematically introduces the technical architecture, data resources, model algorithms and computing resources of GEE, and summarizes the application results of GEE in various fields, hoping to provide GEE users with a quick understanding of the platform Window to help them make better use of the GEE platform to carry out their own application research.

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Research on Identification of Snow-Covered Mountain Glacier based on Deep Learning
Jingjing WANG,Changqing KE,Jun CHEN
Remote Sensing Technology and Application    2023, 38 (6): 1251-1263.   DOI: 10.11873/j.issn.1004-0323.2023.6.1251
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Global warming results that glaciers retreat rapidly. Monitoring and mapping glacier boundary are extremely significant for research on global climate change and predicting related disasters. However, snow covering is the main barrier all the time. Selecting Karakoram subregion as study area, the Landsat 8 OLI, and Senitnel-1 images and DEM data in spring (March 24th, 2019) were utilized. The spectral reflectance of green, red, near-infrared and short-wave infrared bands in Landsat 8 OLI images were selected as the optical image features. The backscattering coefficient of VH polarization channel, the coherence coefficient of VV polarization channel, local incident angle, polarization entropy H and scattering Angle α after polarization decomposition were gained from SAR data and used as SAR features. Topographic features included DEM and slope. These characters were employed as input of models. First, based on U-Net model, experiments compared the accuracies using different-size samples. The 256×256-pixel-size samples were imported to U-Net network model based on different backbone networks (MobileNetv2, VGGNet, ResNet and EfficientNet) and DeepLabv3+ model. Finally, the best one among the above networks was employed to import samples with different feature combinations. Results show: ①Using the bigger training sample with the richer spatial context information can obtain the higher segmentation accuracy and the glacier terminal boundary is more accurate. ②Among the different backbone networks, VGG19 backbone network exhibits the highest accuracy, which is higher than that of DeepLabv3+. Its F1-value is 0.899 6, and the mean intersection over union(mIoU) is 0.875 4, and the overall accuracy is 0.948 4. The recognition effect of shadow, snow melt-water, mist covering and frozen lake area is comparatively good. ③With the decrease in the number of training features, the accuracy also drops. Topographic features can improve the precision rate, while SAR features can increase the recall rate by 4% or so. This study proves the feasibility of the deep learning methods on the identification of mountain glaciers covered by a large amount of snow and provides reliable basis on model selection and parameters setting for rapid and large-scale mountain glaciers mapping.

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Spatiotemporal Fusion of Remote Sensing Images based on Deep Learning and Extraction of Winter Wheat Planting Area
Jüanjüan ZHANG,Yimin XIE,Ping DONG,Shengbo MENG,Haiping SI,Xiaoping WANG,Xinming MA
Remote Sensing Technology and Application    2023, 38 (3): 578-587.   DOI: 10.11873/j.issn.1004-0323.2023.3.0578
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Rapid and accurate winter wheat acreage extraction using remote sensing technology is of great importance for crop yield estimation and food security. Due to problems such as the difficulty of obtaining medium and high resolution time-series images due to revisit cycles, cloud and rain, and the low accuracy of low resolution remote sensing data in extracting crop planting information. In this study, taking Changge City, Henan Province as an example, Landsat 8 and MODIS images were obtained as the dataset during 2015~2020, and the 2 data were fused based on an optimized convolutional neural network spatio-temporal fusion model to construct a 30 m resolution NDVI time series set, and S-G (Savitzky-Golay) filtering was used to denoise the time series set, and finally The area planted with winter wheat was extracted using the RF method. The results show that the optimised fusion model is robust and the R2 of both the predicted and real images is above 0.92. The agreement between wheat area extraction and statistical area in the study area was 97.3% and the results were reliable. Therefore, the optimised model can better fuse the medium and high resolution images, which is an effective technical means to supplement the missing images, and the constructed time series set can more accurately extract the wheat planting area in the county.

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Estimation of Winter Wheat Harvesting Area based on Sentinel-2 Images
Shengwei LIU,Dailiang PENG,Junjie CHEN,Jinkang HU,Zihang LOU,Xuxiang FENG,Enhui CHENG
Remote Sensing Technology and Application    2023, 38 (3): 544-557.   DOI: 10.11873/j.issn.1004-0323.2023.3.0544
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The spatial distribution and winter wheat harvesting area is of great significance to accurately estimate production and ensure food security. However, the vast majority of study and statistical data is based on planting area of winter wheat, and few studies have been done on the winter wheat harvesting areas. In this study, Puyang County was selected as the study area, and the harvesting area of winter wheat was estimated by combining Sentinel-2 remote sensing imagery at the maturing period in 2019 and random forest model. Firstly, best feature subsets were obtained through feature selection. And then, the separability between winter wheat and other land types was analyzed by the J-M distance of these best feature subsets, the harvesting area and planting area of winter wheat were identified and extracted, and the harvesting area of winter wheat was mapped. Finally, the differences in harvesting area and planting area of winter wheat and the influencing factors of harvested area were further analyzed. The results found that the overall accuracy and Kappa coefficient of winter wheat harvested area estimated by the best feature subset of Sentinel-2 images were 94.62% and 0.93, respectively. The planting area of winter wheat in Puyang County in 2019 was 79.47 thousand hectares, and the extracted harvesting area was 76.74 thousand hectares, their difference (2.73 thousand hectares) was largely attributed to human activities, and timely monitoring of the harvesting area of winter wheat can provide a certain scientific reference value for related research and decision-making such as winter wheat yield prediction.

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Application Status and Prospect of Water Storage and Drought Monitoring based on GRACE Data
Jiangdong CHU,Xiaoling SU,Tianling JIANG,Xuexue HU,Te ZHANG,Haijiang WU
Remote Sensing Technology and Application    2023, 38 (5): 1003-1016.   DOI: 10.11873/j.issn.1004-0323.2023.5.1003
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Water storage is a critical component of the global and regional hydrological cycle, which can be used to analyze the spatio-temporal evolution of regional water resources and drought. Traditional methods to monitor water storage are usually based on in-situ groundwater level data. However, challenges arise due to the limited placement and distribution of monitoring stations in large-scale research and exploration. The Gravity Recovery and Climate Experiment (GRACE) satellite have provided large-scale monthly data on Earth's gravity field variation. Several scholars have applied the water storage anomalies data retrieved by GRACE in hydrology research, which has facilitated the progress and development of hydrology. However, the current systematic elaboration of research on inversion of water storage based on GRACE data is not comprehensive enough, and few studies have systematically summarized the status of monitoring drought, interpolation, and reconstruction based on GRACE data. Firstly, this study briefly introduces the application fields of GRACE data, and discusses the advantages and disadvantages of the two data processing methods. Then, the application status and existing problems of GRACE data in the verification and uncertainty of inversion results, terrestrial water storage anomalies, groundwater storage anomalies, drought evolution and response, and interpolation and reconstruction were analyzed and summarized. Finally, further research about GRACE was suggested to carry out in the aspects of exploring the impact of changing environments on water storage anomalies, reducing the uncertainty of GRACE data, constructing a suitable drought index for drought monitoring, improving the accuracy of interpolation and reconstruction, and improving spatio-temporal resolution. The study is aiming to provide reference and insight for related research using GRACE data.

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Remote Sensing Monitoring of Temporal Variation in Cotton Hail Disaster based on Multi-temporal Sentinel-2 Image
Wendong QI,Xüechang ZHENG,Liming HE,Zhen LU,Xiaohe GU,Yanbing ZHOU
Remote Sensing Technology and Application    2023, 38 (3): 566-577.   DOI: 10.11873/j.issn.1004-0323.2023.3.0566
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In recent years, global warming has led to an increase in strong convective weather, and hail disaster has become one of the main disasters in agricultural production. Carrying out remote sensing assessment of cotton hail disaster is of great significance for disaster prevention and mitigation, insurance claims and planting structure adjustment. Taking the cotton hail disaster in the Kuitun River Basin in the southwest of Junggar Basin, Xinjiang, on 23 August, 2019 as the research object, with the support of field measured samples, the multi-temporal Sentinel-2 remote sensing images before and after the hail disaster were obtained. We analyzed the dynamic changes of various vegetation indexes before and after the hail disaster, and screened the sensitive vegetation index difference feature combinations which can effectively characterize the hail disaster. The range and grade of cotton hail disaster were automatically extracted by using machine learning algorithms such as logical regression, decision tree, gradient lifting decision tree and random forest, and the accuracy was compared and analyzed via field measured samples. The results showed that NDVI was the best indicator of hail disaster among single vegetation index, with an overall accuracy of 84.39% and a Kappa coefficient of 0.75. The combination of multi-temporal vegetation index differences was significantly more indicative for hail disaster than that of single vegetation index. Compared with the time series characteristics of vegetation index differences before and after hail disaster, the indicative of hail disaster between August 30 and August 20 was obviously stronger than that between August 25 and August 20, which indicated that it was necessary to consider the self-recovery ability of cotton plants after hail disaster grade for remote sensing monitoring. The combination of the pre- and post-disaster vegetation indices and the random forest classification algorithm was the most effective methods in monitoring the level of cotton hail disaster level, with an overall accuracy of 89.51% and a Kappa coefficient of 0.83. In conclusion, the extent and degree of cotton hail disaster can be effectively evaluated based on multi-temporal Sentinel-2 image.

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High Spatio-temporal Resolution XCO2 Data Interpolation Algorithm based on OCO-2/3 Satellite
Ruonan PANG,Ailin LIANG,Xinyü LI,Xinjie LU
Remote Sensing Technology and Application    2023, 38 (3): 614-623.   DOI: 10.11873/j.issn.1004-0323.2023.3.0614
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.CO2 is one of the important greenhouse gases in the atmosphere. Since the Industrial Revolution,the concentration of CO2 in the atmosphere has been increasing continuously, which has an important impact on global climate change. High precision,high coverage and high temporal and spatial resolution CO2data tends to be more significant in the study of carbon neutral and global CO2 change. Thus, in this study, we compared the XCO2 products between the satellites OCO-2 and OCO-3, and formed a joint data set from the two satellites. Because there are still some regions without observation data in the joint dataset, this study uses Kriging interpolation algorithm to fill the regions without data. Considering the temporal and spatial variation characteristics of CO2 concentration in different latitudes, the algorithm divides theworld into six regions and selects the appropriate variogram.The results show that the XCO2 data coverage increases by 52.32%, 46.77%, 44.04%, and 33.81% on the 3-day, 8-day, 15-day, and 30-day timescales,respectively. By comparing the monthly interpolation data set with the TCCON site data to verify the accuracy, the mean absolute error is 1.049 ppm, the root mean square error is 1.024 ppm, and the coefficient of determination is 0.82. It can be seen that this method can accurately fill in the blank area of the j-oint dataset, and improve the accuracy, coverage and spatiotemporal resolution of the data.

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Landuse Dynamic Monitoring in Open-pit Mining Area based on High-resolution Remote Sensing Image: A Case Study of Pingshuo Mining Area
Peiwei FAN,Mengmeng HAO,Dong JIANG,Fangyu DING
Remote Sensing Technology and Application    2023, 38 (2): 274-284.   DOI: 10.11873/j.issn.1004-0323.2023.2.0274
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Coal energy is an important component of China's energy system,and the open pit mining of coal has a more profound impact on the surrounding environment than well mining. Pingshuo Mining Area is an early open-pit coal mine developed in China, which mainly adopts the mining mode of mining while repairing, resulting in rapid land use changes in the mining area, and it is urgent to efficiently and accurately extract various types of land objects and monitor their ecological restoration. Based on multi-period high-resolution image data, this paper extracted the land use information of the study area from 2013 to 2020 through multi-scale segmentation and machine learning object-oriented classification method, and further analyzed the dynamic change of land use in Pingshuo open-pit mining area. The results show that from 2013 to 2020, the mining area moved eastward year by year, the mining area decreased by 7.84 km2, farmland decreased by 36.08 km2, woodland and grassland increased by 64.77 km2, and water body, dump and mining area decreased by less than 10 km2. The results of the analysis of land use area change in the study area show that the effect of green mine construction is remarkable. Combined with the green development policy of Pingshuo Coal Mine, this study will provide methods and data support for the evaluation of green mine construction.

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Remote Sensing based Land Cover Classification of Pu′er City Using GEE Cloud Platform and Sentinel-2 Data
Ming YAN,Yong PANG,Yunling HE,Shili MENG,Wei WEI
Remote Sensing Technology and Application    2023, 38 (2): 432-442.   DOI: 10.11873/j.issn.1004-0323.2023.2.0432
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Quick and accurate access to the spatial distribution of forests is of great significance for assessing the status of forest resources and ecological environment protection.Taking Pu'er City in Yunnan Province as the research area, Based on the Google Earth Engine (GEE) platform and Sentinel-2 image data,combined with the field survey data, airborne remote sensing data and terrain auxiliary data, the spectral features, texture features and topographic features were extracted. Through feature screening, the optimal feature set suitable for forest classification was obtained.Combining Simple Non-Iterative Clustering (SNIC) superpixel segmentation algorithmto explore the influence of different classification methods and characteristic variables on the classification accuracy.The results showed that the classification accuracy of the object-oriented classification method was higher than that of the pixel-based classification method, with an overall classification accuracy of 88.21% and the Kappa coefficient of 0.87. which can accurately map the forest cover of Pu 'er City. The object-oriented method can effectively alleviate the “salt and pepper phenomenon”, and feature optimization avoids the influence of redundant information on classification results and effectively improves classification efficiency. The combination of GEE platform and object-oriented method can provide large-area, high-precision forest cover remote sensing rapid mapping.

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Trends and Attribution Analysis of Habitat Quality Changes in Beijing-Tianjin-Hebei Region over the Past 40 Years
Yuxing YAN,Yuanyuan YANG,Yongsheng WANG,Jianwu YAN
Remote Sensing Technology and Application    2023, 38 (2): 251-263.   DOI: 10.11873/j.issn.1004-0323.2023.2.0251
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Habitat quality has been widely regarded as a proxy indicator for biodiversity. In the context of the continuous decline of global biodiversity and degradation of ecosystem services, it is of great significance to study regional spatio-temporal trends of habitat quality for its sustainable development. Over the past 40 years, rapid urbanization has profoundly impacted the spatial and temporal distribution and functions of habitats, leading to ecological degradation and the weakening of ecosystem service functions. Exploring the spatial and temporal dynamics and driving factors of regional habitat quality is important for ecological restoration and biodiversity conservation. Based on remote sensing monitoring data of land use in the Beijing-Tianjin-Hebei (BTH) region from 1980 to 2020 (every 10 years), this study used the InVEST model to quantify the regional habitat quality and explore the temporal and spatial evolution characteristic, then it analyzed the response of habitat quality to land use/cover change, socio-economic development, and natural conditions by adopting a Geographically Weighted Regression (GWR) model. The results showed that Habitat Quality (HQ) in the BTH region has shown a decreasing trend during the study period, and the area of low-quality areas has increased significantly, lower-quality areas and medium-quality areas have shown a decreasing trend, while higher-quality areas and high-quality areas are relatively stable. The spatial pattern of HQ in the BTH region was that the northwest areas were relatively high and the southeast areas were relatively low. The habitat quality of eastern coastal areas and western mountainous areas has improved significantly over the past four decades, while the HQ around Beijing, Tianjin, and the southern rapidly developing cities has decreased gradually. The changes of construction land and the topographic index have the most significant impact on the changes of the HQ index. HQ changes were negatively correlated with socio-economic factors such as construction land area, GDP, and population density; and positively correlated with natural environmental factors such as topographic index, precipitation, and temperature. HQ changes were more significantly influenced by the natural environment in the northwestern part of the study area and more significantly influenced by socioeconomic factors in the southeastern plain area. The results help to reveal the response of habitat quality changes to the influencing factors, which can provide scientific basis and theoretical guidance for the implementation of sustainable land use, ecological environmental protection and management.

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Study on the Method to Detect Coastline from Navigation X-band Radar Images
Zhongjüe FAN,Yijun HE,Zhongbiao CHEN
Remote Sensing Technology and Application    2023, 38 (3): 739-751.   DOI: 10.11873/j.issn.1004-0323.2023.3.0739
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The change of coastline affects the production and life of coastal residents and the national military strategic deployment. It is of great significance to detect the change of coastline quickly and accurately. Navigation X-band radar can continuously and real-time detect the nearshore marine environment. This paper proposes a method to detect coastline using navigation X-band radar images. Firstly, the navigation X-band radar image is preprocessed by averaging, contrast enhancement and Gaussian filtering to reduce the impact of sea clutter on the radar image; Then, the preprocessed image and the edge detection operator are convolved to obtain the gradient image, and an adaptive threshold estimation method based on the histogram bimodal method is established to automatically extract the boundary of the target island from the gradient image; Finally, the edge expansion, cavity filling and denoising are used to extract the complete island, and the area and perimeter of the island are estimated. The proposed method is verified by using the navigation X-band radar images observed in the experiment. Compared with the coastline in the electronic chart and Google Earth map, the edge accuracy of the target island extracted by Sobel operator and Prewitt operator is higher, and the area and perimeter of the extracted island vary with the tide height, sea conditions, etc. The results show that the navigation X-band radar can effectively monitor the real-time changes of the island coastline.

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Study on Haze Region Identification in North China Plain based on Random Forest Algorithm
Likun ZHANG,Yifan PAN,Chuwen ZHAO,Guoliang QIU,Pei ZHOU,Xiang CHEN,Yang WANG
Remote Sensing Technology and Application    2023, 38 (3): 752-766.   DOI: 10.11873/j.issn.1004-0323.2023.3.0752
Abstract171)   HTML0)    PDF(pc) (5069KB)(151)       Save

Atmospheric haze pollution is one of the major environmental problems facing China in recent years. Haze monitoring is an important part of haze pollution governance system, and remote sensing can realize large-scale and long-term dynamic monitoring, which to a certain makes up for the shortcomings of traditional site monitoring methods. Based on MODIS/Terra data, this study takes seven provinces and municipalities in North China Plain as the study area, built a 10-dimensional feature space by using the spectral and spatial characteristics of atmospheric haze, and built a regional identification model of haze using random forest algorithm. This model not only realizes the function of haze region identification and extraction, but also realizes the function of light and heavy haze region classification. Through verification of PM2.5 monitoring data from ground stations, the overall accuracy of haze region identification is 87.82%, and the Kappa coefficient is 0.75. The overall accuracy of light and heavy haze region identification is 86.81%, and the Kappa coefficient is 0.73. The study results show that the model has a good identification effect on haze region in satellite images, which can provide data support for air pollution monitoring, and has certain reference significance for monitoring other atmospheric pollutants.

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Estimation Model of Spartina alterniflora Aboveground Biomass by Remote Sensing in Zhejiang Coastal Wetland
Xiaowu YANG,Weidong MAN,Mingyue LIU,Yongbin ZHANG,Hao ZHENG,Jingru SONG,Zhiqiang KANG
Remote Sensing Technology and Application    2023, 38 (6): 1445-1454.   DOI: 10.11873/j.issn.1004-0323.2023.6.1445
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Estimation of Aboveground Biomass (AGB) in Spartina alternifloraS.alterniflora) can provide an important basis for ecosystem stability evaluation and regional carbon sink assessment in coastal wetlands. Using typical coastal wetlands in Zhejiang, China as an example, this study used 48 measured aboveground biomass data of S.alterniflora to extract vegetation index and band characteristics reflecting aboveground biomass information of vegetation based on Landsat8 OLI images, constructed a Univariate Regression (UR) model, a Multiple Linear Regression(MLR) model, a multi-scale geographically weighted regression model (MGWR), and a Partial Least Squares Regression (PLSR) model to estimate the AGBof S.alterniflora from actual field measurement data. The results showed that: (1) The AGBof S.alterniflora was significantly correlated with the 29 selected remote sensing variables, and the correlation coefficients were all between 0.5 and 0.8(P<0.01). (2) The model constructed by PLSR method was the optimal model of S.alterniflora AGB inversion in the Zhejiang coastal wetland (R2=0.767; RMSE=130.576 g/m2; MAE=100.801 g/m2). (3) The average AGB of S.alterniflora in Zhejiang coastal wetland was 6 607.01 g/m2, and the total AGB was 1.36×103 t. The distribution pattern of S.alterniflora AGB in the coastal area of Zhejiang Province was high in the south and low in the north. This study could provide the scientific basis for the rational development and utilization of coastal wetland resources, carbon sink monitoring, and ecosystem function evaluation.

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Estimation of Forest Canopy Closure based on Multi-source Remote Sensing Data and Geometric Optical Model
Pengjie WANG,Xin TIAN,Shuxin CHEN,Yong SU,Haiyi WANG,Chao MA
Remote Sensing Technology and Application    2023, 38 (2): 383-392.   DOI: 10.11873/j.issn.1004-0323.2023.2.0383
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Forest canopy closure is an important factor in evaluating forest resources and accurate estimation of forest canopy closure is of great significance to forest management. Based on Li-Strahler geometric optics model, we estimated the forest canopy closure using Unmanned Aerial Vehicle (UAV) LiDAR and GF-6 WFV data. And in order to find a way out of the problem of mixed pixels, a reliable method was proposed. Firstly, the sunlit background component within the coverage of UAV LiDAR was calculated by using the high-precision forest structure parameters derived from UAV LiDAR. Then, the SMACC algorithm and linear spectral decomposition model were used for mixed pixel decomposition of GF-6 WFV and to determine the optimal scene component in the study area. Finally, the forest canopy closure in the study area was estimated by Li-Strahler geometric optical model, and the accuracy was verified by the measured data of field sample plots. The results showed that the determination coefficient (R2) between the estimated canopy closure and the measured canopy closure is 0.692 8, the Root Mean Square Error (RMSE) is 0.059 4, and the overall accuracy is 93.4%. Li-Strahler geometric optical model can effectively play a role in the inversion of forest canopy closure.

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Crop Classification Method from UAV Images based on Object-Oriented Multi-feature Learning
Mengting JIN,Qüan XÜ,Peng GUO,Baohua HAN,Jun JIN
Remote Sensing Technology and Application    2023, 38 (3): 588-598.   DOI: 10.11873/j.issn.1004-0323.2023.3.0588
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Obtaining high-resolution image features of crops and exploring the influence of multi-feature learning on actual crop classification are of great significance for agricultural departments to grasp the information of crop planting fine structure and efficiently implement production management. For the high-resolution RGB image acquired by UAV, this paper proposes a new classification method of crops based on object-oriented and multi-feature learning. Firstly, the HSI model is used to transform the colour space of RGB images to mine the potential information of images further. Secondly, the ESP algorithm and CART are used to determine the optimal image segmentation scale and construct the optimal feature learning data set of classification. Finally, object-oriented Random Forest classification algorithm was used to learn and train the multi-feature space, so as to achieve fine crop classification, and accuracy evaluation was carried out in combination with validation data set. The experimental results show that the overall accuracy of the classification in the study area reached 90.18%, and the Kappa coefficient reached 0.877, both of which were greater than the accuracy based on pixel-level and single-feature learning. The optimal feature learning space constructed in this paper have a good classification effect on cotton, corn, cocozelle, grape and other major crops in the study area. The producer's accuracy of each crop type is greater than 89%. This research can provide a reference for agricultural precision management and planting structure optimization.

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Study on Decomposition of Large Area based on Multi-factor Superposition for Remote Sensing Photography
Xiangqiang MENG,Feng LI,Xing ZHONG,Xiaobin YI,Songyan WEI
Remote Sensing Technology and Application    2023, 38 (4): 767-775.   DOI: 10.11873/j.issn.1004-0323.2023.4.0767
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Regional target decomposition is a key part of remote sensing satellite imaging mission planning for large area coverage, which is of great significance to quickly acquire effective data in large area. Based on the planning experience of satellite photographing in large area, the main influencing factors in the actual operation of large area photographing are summarized, including satellite orbit transit, regional cloud cover at transit window and real-time update of base image data, and a large area photographing decomposition method based on multi-factor superposition is proposed. In this method, the cloud cover factor is applied to the decomposition of the strips in each transit window, and the better photographed strips in each satellite transit are obtained based on the idea of greedy algorithm, which can improve the single data acquisition efficiency and the overall coverage efficiency. This method has been applied to the project of "Data Cube for large coverage datasets of Chinese high resolution and broad band and multispectral satellite constellation", which provides support for Jilin-1 GP01/GP02 satellite to quickly acquire data in the 65 countries and regions along the Belt and Road. By comparing the acquisition of regional coverage data before and after using the method, the data coverage efficiency was improved by about 44%.

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Comparison of GF-1 and Sentinel-2 for Estimation of Leaf Area Index in Typical Crops
Yangfan ZHOU,Xingming ZHEN,Yuan SUN,Zui TAO,Zewen DAI,Chi XU,Lin LIU,Yanling DING
Remote Sensing Technology and Application    2023, 38 (3): 599-613.   DOI: 10.11873/j.issn.1004-0323.2023.3.0599
Abstract164)   HTML0)    PDF(pc) (6023KB)(117)       Save

The launch of the Gaofen-1 satellite has further enhanced China's Earth observation capabilities. Compared with Sentinel-2, the GF-1 WFV image has a high spatial resolution of 16 m, but lacks the Red-Edge (RE) band and the Short-Wave Infrared (SWIR) band. It is important to analyze the differences in the accuracy of estimating vegetation physiological parameters between the two satellites for further application. In this study, we used linear regression models and a Look-Up Table (LUT) based on the PROSAIL model to assess the performance of GF-1 and Sentinel-2 in estimating LAI of soybean and maize. The results showed that: (1) The EVI simple linear regression of GF-1 outperformed other vegetation indices with a R2 value of 0.81 and the MNLIre model was the best Sentinel-2 model with a R2value of 0.86. (2) GF-1 obtained a comparable accuracy to Sentinel-2 with multiple linear regression models based on spectral bands. The best LAI estimation model of GF-1 produced a Root-Mean-Square Error (RMSE) of 0.54 and a coefficient of determination (R2 ) of 0.90, and the best Sentinel-2 model achieved a RMSE of 0.54 and R2 of 0.89. (3) In terms of LUT based on the PROSAIL model, the optimal band combination for GF-1 were B2 and B4 with a R2 of 0.76 and a RMSE of 0.81, and the optimal band combinations for Sentinel-2 were B3, B6, B7, B8, B8a, and B12 with a R2 of 0.87 and a RMSE of 0.62. This study showed that GF-1 satellite has the ability to accurately monitor crop LAI, which can provide a theoretical basis for the application of GF-1 in agriculture monitoring.

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The Feasibility Analysis on Satellite Data based Crop Fungal Toxin Prediction
Jihua MENG,Hegang ZHENG,Songxüe WANG,jin YE
Remote Sensing Technology and Application    2023, 38 (3): 535-543.   DOI: 10.11873/j.issn.1004-0323.2023.3.0535
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Mycotoxin is major threat to food security and safety in China, and their accurate prediction can support effective loss control and reduction. Based on the summary of the impact of crop mycotoxins on food, food and agriculture, this paper analyzes the current research progress of mycotoxin prediction from three aspects: meteorological statistical model, mechanistic model and machine learning model.The feasibility of using satellite remote sensing monitoring technology to carry out a wide range of crops mycotoxin prediction is discussed by analyzing the current influencing factors of crop contamination mycotoxins and combining with the research progress of remote sensing technology in monitoring crops and their growing environment.It is also pointed out that analyzing the main influencing factors of crop mycotoxins from remote sensing and their change patterns, developing remote sensing estimation methods of mycotoxins by combining relevant factors within the reproductive period, and coupling remote sensing technology oriented to farm-scale dynamic prediction with multiple models will become the research focus in this field.

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Terrain Differentiation and Driving Mechanism of Land Use Landscape Pattern
Zhenqi YANG,Mingyou MA,Jianlin TIAN
Remote Sensing Technology and Application    2023, 38 (5): 1226-1238.   DOI: 10.11873/j.issn.1004-0323.2023.5.1226
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Studying the topographic differentiation characteristics and driving mechanism of land use landscape pattern is of great significance for land use optimization and landscape dynamic management. Yongding District of Zhangjiajie City, with complex terrain, various types of coverage and tourism interference, was selected as the research object. The landscape type maps of multiple years in the study area were superimposed one by one with elevation, slope and aspect classification maps, and classified and counted. Eight landscape indices such as Patch Density(PD), Aggregation Index(AI), and contagion index(CONTAG) were selected from the landscape level index and type level index to calculate the annual change of the index and explore its topographic differentiation law and driving mechanism. The results show that : (1) The land use landscape types in the study area have obvious altitude gradient characteristics. More than 80 % of the land area is concentrated in the area with an altitude of 300 ~ 800 m and a slope of 6 ° ~ 35 °. (2) Whether the landscape level index or the type level index, the topographic differentiation characteristics are obvious, and the differentiation of elevation and slope is significantly higher than that of slope direction. (3) The evolution of land use landscape pattern in the area with large terrain gradient ( high altitude steep slope area ) is dominated by natural ecological evolution, while the evolution of the area with small terrain gradient ( low altitude gentle slope area ) is obviously disturbed by social and economic factors.

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GNSS-INSAR Fusion Method for High Precision Monitoring of Surface Deformation
Fuyang KE,Xiangxiang HU,Lulu MING,Xuewu LIU,Jixin YIN,Yuhang LIU
Remote Sensing Technology and Application    2023, 38 (5): 1028-1041.   DOI: 10.11873/j.issn.1004-0323.2023.5.1028
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Surface deformation is a geological phenomenon caused by natural or artificial factors, and its disaster-causing process is slow and irreversible. It is also a geological disaster with destructive solid power. Therefore, real-time and high-precision surface deformation monitoring is one of the most critical tasks in maintaining urban safety. However, due to the complex causes, long duration, wide range, and many triggering factors of surface deformation, there are many difficulties in monitoring surface deformation using single technology such as leveling, GNSS, INSAR, and optical remote sensing. Considering the characteristics and complementarities of InSAR and GNSS, the combination of InSAR and BeiDou/GNSS can improve the surface deformation monitoring capability in space and time at the same time. Unluckily, the traditional GNSS-InSAR data fusion method is simple to fuse and cannot dynamically reflect surface deformation characteristics, leading to insufficient data use and low accuracy of deformation features. A new fusion method is proposed based on the Kalman filter algorithm GNSS-InSAR correction values. The method mainly consists of two sequential processes, i.e., the a priori processing of GNSS and INSAR data and the fusion process of GNSS-InSAR correction values based on the Kalman filter algorithm. The a priori processing of GNSS and INSAR data is to obtain the a priori deformation results using the fitted estimation model to correct the systematic errors in the InSAR observations. The fusion process of GNSS-InSAR correction values based on the Kalman filtering algorithm is to fuse the two data through Kalman filtering based on the spatial and temporal correlation between the time-series GNSS observations and the InSAR correction observations. The experiment was processed using 103 views of sentinel-1A data from November 15, 2018, to June 3, 2022, and 13 GNSS point data during the same period. The experimental results show that the fusion result of the corrected InSAR observations and GNSS observations by the Kalman filter is 45% more accurate than the fusion result of the uncorrected InSAR observations and the GNSS observations, which is 45% 57% higher than the accuracy of InSAR observations. Therefore, the fusion method model based on the Kalman filter algorithm of GNSS-InSAR corrected values proposed in this paper improves the accuracy of InSAR deformation monitoring and expands the breadth and depth of InSAR applications.

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GPU Parallel Fast Geometry Correction of FY-3D MERSI data based on TensorFlow
Weidong WANG,Qüan Wenting,Zhao Wang,Hui Zhou
Remote Sensing Technology and Application    2023, 38 (3): 671-679.   DOI: 10.11873/j.issn.1004-0323.2023.3.0671
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Based on the TensorFlow framework, which has the characteristics of GPU or CPU parallel acceleration calculation on tensor (multidimensional arrays). The WGS84 latitude and longitude projection is selected, combined with FY-3D MERSI L1 data with high-precision and same-resolution positioning data, to generated tensors (multidimensional array) to align latitude and longitude data according to resolution, and calculated the new image pixel mapping position information. According to the position index information, the MERSI data can be geometric corrected point by point, and the BowTie effect caused by the scanning observation and earth curvature of medium-resolution polar orbit satellites can be eliminated at the same time. Finally, the convolution is used to calculate the inverse distance weighted interpolation point values and fill the pixels with no data after geometric correction. Using this method, the author implemented geometric correction of all 25 channels of FY-3D MERSI data in Python under the TensorFlow framework. Compared with the geometric correction results with ENVI software as the standard. The error and correction precision are calculated, and the overall processing speed of geometric correction is also tested. The results show that the algorithm proposed in this paper has a high consistency with the geometric correction of ENVI software, and the accuracy below 5% absolute error percentage is greater than 0.92, and the structural similarity SSIM index is around 0.95. Speedup is more than 36 times to complete geometric correction for all channels using GPU parallel acceleration. In summary, the geometric correction method adopted in this paper is fast and efficient in processing, and ensures the accuracy of correction.

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Dynamic Semantic Extraction of Urban Blocks Activity based on Topic Model
Rui XIAO,Yuxiang GUO,Xinghua LI
Remote Sensing Technology and Application    2023, 38 (3): 649-661.   DOI: 10.11873/j.issn.1004-0323.2023.3.0649
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As the functions of urban areas become more and more complicated, it is of great significance to identify the specific function types of urban blocks scientifically and accurately. This paper presents a time-series dynamic urban functional area recognition scheme. Taking the area within the Sixth Ring Road of Beijing as the research area, the high incidence area of travel mode is extracted from the massive travel data by using taxi trajectory data and Dynamic Topic Model (DTM). Urban blocks are clustered based on topic model feature. The research use POI semantic annotation clustering results to identify urban functional areas. This paper studies and evaluates the change trend and distribution of topic blocks during six years, and discusses the dynamic changes of semantics of blocks: (1) The dynamic topics distribution has spatial diffusion, and the distribution of block semantic intensity shows obvious circle expansion. (2) The spatial boundary of clusters based on travel activities gradually coincides with the administrative divisions of the study area over time, and the function labeling results are highly matched with the specific functions of the area. (3) The high value of topic variation value is mainly distributed in the outer ring area, and has a negative correlation with the proportion of construction land. This research shows that the dynamic topic model is applicable in the travel data mining scenario, providing a new reference direction for the application of dynamic topic model in the field of mobile data mining.

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Improved Remote Sensing Rotating Object Detection based on CenterNet
Xin LIU,Jin HUANG,Yingwei YANG,Jianbo LI
Remote Sensing Technology and Application    2023, 38 (5): 1081-1091.   DOI: 10.11873/j.issn.1004-0323.2023.5.1081
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Remote sensing images have low detection accuracy due to the characteristics of different object angles, generally arranged densely, high proportion of small objects and complex background. In view of the inapplicability of the horizontal detection algorithm for remote sensing rotating object detection, and the periodicity and edge interchangeability of angle in the mainstream five-parameter method, a VR-CenterNet is proposed, which used the vector representation to detect the rotating box and design the loss function to avoid the problem of angle regression, and to optimize the high displacement sensitive problem of slender objects. For the high redundancy problem of shallow feature fusion, self-adaptive channel activation is introduced to automatically filter impurity information. In order to strengthen the key point information, an improved global contextual self-adaptive layer activation attention block is introduced in the output of backbone. First, the performance of different algorithms is compared on HRSC2016 and UCAS-AOD data sets. Then, the module ablation experiment is conducted on the two data sets to verify the effectiveness of each improved method. Experimental results show that: 88.48% and 90.35% accuracy are obtained on HRSC2016 and UCAS-AOD data sets respectively. The improved algorithm can improve the detection accuracy of remote sensing rotating objects, and provide another problem-solving idea for the accurate detection of remote sensing rotating objects.

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Spatial Downscaling of GPM Satellite Precipitation Products in the Yangtze River Basin, China
Yi DU,Zequn LIN,Shengjie ZHUANG,Runting CHEN,Dagang WANG
Remote Sensing Technology and Application    2023, 38 (3): 697-707.   DOI: 10.11873/j.issn.1004-0323.2023.3.0697
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Satellite precipitation products with a high spatio-temporal resolution are essential for hydrometeorological research on the regional or watershed scale. By comparing the accuracy of IMERG and GSMaP satellite precipitation products in the Yangtze River basin during 2015~2020, the outperformed product is then used for spatial downscaling. Considering the relationship between precipitation and geographic features (DEM), vegetation index (NDVI), and Land Surface Temperature (LST), various statistical downscaling models based on random forest regression are developed, such as DEM model, DEM+NDVI model and DEM+NDVI+LST model. Then, the performance of the three models is further evaluated against observations from 133 stations in the study area. The results indicate GSMaP outperforms IMERG in the Yangtze River basin. Among the three downscaling models, DEM+NDVI+LST model performs best on the multi-year mean scale and annual scale, and the performance of the model remains stable on the monthly scale. This study can provide a new way for spatial downscaling of satellite precipitation products.

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Analysis of Vegetation Cover Change Characteristics and Influencing Factors in the Shiyang River basin based on GEE
Chunshuang FANG,Rui ZHU,Rui LU,Zexia CHEN,Lingge WANG,Jian’an SHAN,Zhenliang YIN
Remote Sensing Technology and Application    2023, 38 (5): 1167-1179.   DOI: 10.11873/j.issn.1004-0323.2023.5.1167
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As an important part of terrestrial ecosystems, vegetation is often used as an indicator to assess the effectiveness of climate change and ecological restoration. In this study, the Shiyang River Basin is taken as an example, Theil-Sen and Mann-Kendall models, and the Hurst index were used to analyze the change characteristics of vegetation cover. The correlation analysis, residual analysis and Geodetector were used to explore the influencing factors of vegetation cover change. The results showed that the vegetation NDVI demonstrated a fluctuating but upward trend from 2001 to 2020, with a rate of increase of 0.023/10 a. Areas with significant increased and significant decreased accounted for 72.32% and 2.4%, respectively. Areas with sustainability (Hurst>0.5) accounted for 63.84 % of the entire area, among which 47.37% showed continuously significant increasing trend. The correlation results between NDVI and climatic factors indicated that the impact of precipitation was particularly significant, and the impacts of temperature, solar radiation and saturated vapor pressure deficit were relatively weak. The area of NDVIpre showed a significant increase trend accounted for 21.59%, while the area of NDVIres showed a significant increase trend accounted for 60.07%, so interannual variation of NDVI in Shiyang River Basin was greatly affected by human activities. Geodetector results showed that the spatial distributation characteristics of water-heat conditions. It is noted that the spatial distribution of NDVI of cultivated land is greatly affected by population density. The results of this study are helpful to deepen the understanding of the driving factors of vegetation change and provide scientific reference for ecological protection and restoration of Shiyang River Basin.

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Research on Extraction Method of Single Tree Height from UAV Oblique Images Broad-Leaved Forest based on GIS Neighborhood Analysis
Mengguang LIAO,Meng LI,Nan CHU,Shaoning LI
Remote Sensing Technology and Application    2023, 38 (5): 1203-1214.   DOI: 10.11873/j.issn.1004-0323.2023.5.1203
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UAV remote sensing technology can quickly obtain the Canopy Height Model(CHM) of the survey area. How to identify tree vertices more accurately from CHM is key to tree height extraction. This paper discusses the influence of different window types, window sizes, and stand canopy density on the extraction of tree vertices. Using the university campus as the study area, two local areas of dense and sparse forest land were selected based on canopy density. GIS rectangular neighborhood analysis, GIS circular neighborhood analysis, and local maximum algorithm are used to extract tree vertices. The results show that the accuracy of tree vertex extraction is not only affected by the window size and canopy density, but also closely related to the window type, and the result of GIS rectangular neighborhood analysis to extract tree vertices is more stable and accurate, and the highest F-Measure value is 78.13% in dense forest, and 96.94% in sparse forest. Comparing the extracted tree heights corresponding to the tree vertices obtained based on this result with the tree height values measured in the field, the RMSE is 37cm for dense forest and 39cm for sparse forest. The results proved the feasibility of extracting tree heights of broad-leaved forests with higher canopy density based on the visible light remote sensing technology of small UAVs, which provided a reference for the subsequent identification of tree vertices based on the canopy height model and improved the accuracy of tree height extraction.

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LightGBM based Impervious Surface Area Extraction of Cities from Arid Areas in Central Asia Using Synthesized Multi-features of Multispectral-SAR Images
Ximing LIU,Alim SAMAT,Wei WANG,Jilili ABUDUWAILI
Remote Sensing Technology and Application    2023, 38 (2): 319-331.   DOI: 10.11873/j.issn.1004-0323.2023.2.0319
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Impervious surface is an important factor indicates the level of urbanization and the urban ecological environment, and it is one of the current research hotspots in urban remote sensing. Compared with humid and semi-humid areas, urban vegetation coverage in arid areas is relatively low, the similar spectum between impervious surface and barren area makes the traditional optical image-based spectral mixing analysis method and spectral index method not suitable for the impervious surface extraction of cities in arid areas. In response to this problem, a method for impervious surfaces extraction of cities from arid areas in Central Asia using synthesized multi-features of multispectral-SAR images is proposed to improve the mixclassifiation between impervious surfaces and bare soil, so as to extract impervious surface in arid area. In detials, Sentinel-2 and the dual-polarization SAR image of Sentinel-1 are selected for three Central Asia cities, Astana, Tashkent and Dushanbe. The spatial characteristics of multi-spectral and SAR images, and the polarization characteristics of SAR are feeded to LightGBM algorithm to classify and extract impervious surface. This paper compares the impervious surface extraction results of different feature combinations and different classification methods. Experimental results indicated that the multi-feature synthesis method of multispectral and SAR images proposed can effectively improve the accuracy of impervious surface extraction in arid areas, indicating the improvement in the misclassification of impervious surface and other land cover types in arid areas; the LightGBM algorithm has higher accuracy than XGBoost, HistGBT and other algorithms based on gradient boosting decision trees and random forest algorithm, and it is more suitable for extraction of impervious surface in arid area. This shows that the method based on LightGBM and the combination of multispectral and SAR multi-features can effectively extract the urban impervious surface in the arid area of Central Asia.

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Semi-automatic Update of the Second Chinese Glacier Inventory based on Deep Learning
Shihao WANG,Changqing KE,Jun CHEN
Remote Sensing Technology and Application    2023, 38 (6): 1264-1273.   DOI: 10.11873/j.issn.1004-0323.2023.6.1264
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Some of the data of the second Chinese glacier inventory(CGI2.0) are replaced by the first Chinese glacier inventory, and these data are concentrated in southeastern Tibetan Plateau. Where the terrain is steep, the climate is harsh, and it is covered by clouds all the year round. There is no systematic glacier survey due to the inability to obtain effective optical images. Aiming at the problems that the traditional threshold segmentation method is influenced by noise, and the standard Unet has a large amount of computation, which leads to slow operation, compressedUnet model is designed to improve model training efficiency and glacier extraction accuracy by modifying model parameters such as sample size, number of convolution kernel and optimizer. Using the polarization characteristics and topographic features of glaciers, 45-scene ENVISAT ASAR images and NASA DEM are selected to carry out deep learning based on Unet and compressed Unet. By referring to optical images and other auxiliary data, the misclassified and missed glaciers are visually interpreted one by one. Finally, the extraction and correction of the glacier boundaries without update are completed, and their attributes are updated. The results show that deep learning based on SAR images and topographic features can effectively identify glaciers in cloud-covered areas. In the areas where the CGI2.0 is not completed, there are 8 374 glaciers with a total area of 5 622.65±303.58 km2, and the error accounts for 5.4% of the total glacier area, most glaciers are retreating and fragmenting. The dataset updates the alternative data in CGI2.0, and provides reliable data support for related studies of glacier changes and mass balance in southeastern Tibetan Plateau.

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Random Forest Extraction of Impervious Surface Using Multiple Features of Sentinel Optical and SAR Images
Kaixin KUANG,Yingbao YANG,Yongnian GAO,Yuxiang LIU
Remote Sensing Technology and Application    2023, 38 (2): 422-431.   DOI: 10.11873/j.issn.1004-0323.2023.2.0422
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The accurate extraction of impervious surface is of great significance for regional population density estimation, environmental assessment, disaster prediction, hydrological model construction, urban heat island effect research and climate change analysis. Traditional large scale impervious surface extraction methods are mainly limited by the quality of remote sensing data and the selection of extraction features, and the spatial resolution of extracted impervious surface is low, which is difficult to meet the refined requirements of impervious surface at the present stage. In this paper, based on Sentinel-1 SAR and Sentinel-2 MSI remote sensing data, multiple extraction features of impervious surface were selected from three dimensions, including spectrum, texture and time sequence, to build an impervious surface extraction model based on random forest. In addition, GEE platform was used to carry out extraction experiment of 10m impervious surface in Yangtze River Delta region in 2020. The results showed that in different types of experimental areas, compared with spectral features, spectral features and time series features, the overall accuracy and Kappa coefficient of the proposed method were increased by 5%,9% and 2%,6%, respectively, and all cities with different impervious surface coverage levels had good extraction effects. The overall accuracy and Kappa coefficient of impervious surface extraction at the global scale in the Yangtze River Delta region were 93.75% and 0.88, respectively. The impervious surface area was 6 1591.38 km2, accounting for about 17% of the total area. The impervious surface extraction method proposed in this paper for 10m resolution remote sensing images is suitable for different types of areas such as mountainous areas, rural areas, urban areas and urban fringe areas. The method is simple and easy to operate, has high precision, and is suitable for cloud platform large-area computing.

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Spatiotemporal Variations of NDVI and the Analysis of Its Climate Driving Factors in Hainan Island During 1982~2015
Jiahao CHEN,Zhongmin HU,Kai WU
Remote Sensing Technology and Application    2023, 38 (5): 1071-1080.   DOI: 10.11873/j.issn.1004-0323.2023.5.1071
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To reveal the long-term variation trend of vegetation cover and further determine the main climatic driving factors affecting vegetation variation in Hainan Island. Also, to provide scientific evidence related to the impact of climate change on vegetation and scientific basis for achieving vegetation optimum development in island regions. The spatiotemporal variation trend of vegetation in Hainan Island from 1982 to 2015 was explored by applying trend analysis method to the GIMMS NDVI data. The effects of temperature, precipitation, and solar radiation on vegetation variability in Hainan Island were investigated by partial correlation analysis and principal component regression analysis over the 34 years. Results show that: ①Spatially, vegetation exhibited a significant increasing trend in the northern and coastal regions of Hainan Island while displayed a degeneration trend in Sanya city and its surrounding areas. ②Temporally, we found the vegetation in Hainan Island showed a slowly increased trend in most areas with a speed of 0.019/10 a and its intra-annual variability was obvious. ③In general, temperature and solar radiation jointly dominate the vegetation growth in 88% areas of Hainan Island in a significant way. Solar radiation was the most important climate driving factor to control vegetation variability in Hainan Island, followed by temperature, and precipitation had a small impact on the vegetation variability. ④Temperature dominated the vegetation variability in the northern and western areas of the Hainan Island. By contrast, solar radiation dominated the vegetation variability in the southern areas of the Hainan Island. Precipitation was the dominant climate driving factor to explain the variability of forests in the middle of the Hainan Island. Overall, this study found that temperature and solar radiation were two major climate driving factors which affected vegetation growth in the Hainan Island.

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Study on the Spatialization of Carbon Emission in Xi'an based on the Luminous Data of Luojia-01
Yao ZHANG,Yuxin ZHANG,Yongjian ZHANG,Chao GONG,Yaqian KONG
Remote Sensing Technology and Application    2023, 38 (4): 869-879.   DOI: 10.11873/j.issn.1004-0323.2023.4.0869
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Based on the night lighting data of Luojia 01 and the energy statistics of Xi'an, combined with the ArcGIS spatial analysis method, this paper uses the high oligomeric model to spatially simulate the carbon emission of Xi'an in 2018, calculate and classify the carbon emission intensity of all districts and counties in the city, and study the distribution characteristics of carbon emission of all districts and counties in Xi'an. The results show that there is a good correlation between Luojia-01 light data and carbon emissions, the linear correlation coefficient is 0.720 3, and the correlation coefficient of quartic function polynomial is the highest, which is 0.843 5; In terms of annual carbon emissions, Xi'an's carbon emissions show the spatial distribution characteristics of high in the central main urban area and low in the surrounding counties, which is a cluster distribution, and the clustering results are clustered in the high value area; There are many low-carbon emission intensity districts and counties in the city, and there are a few high-carbon emission intensity districts and counties. The industrial structure needs to be further adjusted to realize the green development model.

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The Analysis on the Evolution of Temporal and Spatial Characteristics of Impervious Surface in Shanghai from 1985 to 2020
Jun MI,Xiao ZHANG,Liangyun LIU
Remote Sensing Technology and Application    2023, 38 (2): 297-307.   DOI: 10.11873/j.issn.1004-0323.2023.2.0297
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The distribution and change of impervious surface is an intuitive sign and important indicator of the urban development process, and it plays an important role in urban ecological functions, urban planning and sustainable development research. This study uses the long time series 30m impervious surface dynamic data set (GISD30) from 1985 to 2020 developed by the Institute of Aerospace Information Research Institute of the Chinese Academy of Sciences, combined with GIS spatial statistical methods, to study and analyze the temporal and spatial evolution of impervious surfaces in Shanghai over the past 35 years.The results show that :(1) Over the past 35 years, the impervious surface area of Shanghai has increased from 878.07 km2 to 2849.90 km2, and the area has expanded to 3.25 times the original size. With the development and opening of the Pudong New Area in 1990, the urbanization process of Shanghai has accelerated significantly. The expansion of impervious surfaces in Shanghai was the most significant from 1990 to 2010. After 2010, the expansion speed and intensity of impervious surfaces began to decline significantly. (2) From the perspective of location differentiation characteristics, the rapid expansion of impervious surface area is mainly located in the suburbs of the city. Among them, the expansion rate of Pudong New Area in each study period is faster than that of other districts. (3) Based on the compactness and fractal dimension, it is found that the spatial distribution structure of impervious surfaces in the central urban area tends to be evacuated, the complexity of the overall impervious surface boundary in Shanghai is reduced, and the urban spatial form is more regular. (4) The urban development layout of “North and South Wings” in Shanghai is relatively obvious. The high-intensity expansion of the southern suburbs has driven the continuous southward shift of the impervious surface space. After 2010, the layout of the impervious surface space in Shanghai has begun to stabilize. At present, under the background of tight resource and environment constraints, Shanghai's urban development faces many challenges. This study has reference value for the effective promotion of Shanghai's urban renewal work.

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Evaluation of Monitoring Ability of GPM Satellite Precipitation Products for Extreme Precipitation over Sichuan Province
Xiaorui YANG,Suikang ZENG,Zhipeng LIN
Remote Sensing Technology and Application    2023, 38 (6): 1496-1508.   DOI: 10.11873/j.issn.1004-0323.2023.6.1496
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Sichuan is one of the regions with frequent extreme precipitation in China. Based on the grid precipitation dataset (CMPA) provided by China Meteorological Administration as the reference of surface precipitation. We systematically evaluated the accuracy of extreme precipitation monitoring in Sichuan province by using a variety of extreme precipitation index including GSMaP (NRT, MVK, Gauge) and IMERG (Early, Late, Final) satellite precipitation products. The results are as follows: (1) The frequency and intensity of extreme precipitation in the basin and its surrounding areas are significantly higher than those in other areas. There is an obvious boundary line of extreme precipitation along the basin and the plateau. (2) IMERG products can detect rainstorm, continuous precipitation events and drought events more accurately, of which Final product have the highest precision, and the precision of extreme precipitation index of IMERG products is significantly better than GSMaP. (3) The results of probability density distribution function (PDF) show that the PDF characteristics of IMERG-Final products and CMPA are the most similar, followed by GSMaP-Guage, However, the Gauge product corrected by the site completely changed the PDF characteristics of NRT and MVK, ignoring many heavy rain and rainstorm events, which will be extremely unfavorable to the monitoring of extreme precipitation. In general, the detection accuracy of GPM products for extreme precipitation has great regional differences. IMERG products show higher extreme precipitation detection accuracy than GSMaP. We also found that there are large precipitation errors in IMERG and GSMaP products in the Western Sichuan Plateau with complex terrain.

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Effects of Forest Disturbance on NPP in the North of the Greater Khingan Mountain
Jingyu ZHANG,Rui SUN,Yanchen BO,Hongmin ZHOU,Helin ZHANG,Qi LI
Remote Sensing Technology and Application    2023, 38 (2): 413-421.   DOI: 10.11873/j.issn.1004-0323.2023.2.0413
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Forest disturbance affects the carbon cycle and carbon balance of forest ecosystem, and vegetation Net Primary Productivity (NPP) is an important indicator of vegetation carbon sequestration capacity. Analyzing the effects of forest disturbance on NPP is of great significance for global change research and ecosystem carrying capacity and resilience assessment. The forest coverage in the northern section of the Greater Khingan Mountain is more than 70%, which has been disturbed by human and natural factors for a long time. This region was selected as the study area in this paper. The 30 m resolution time series NPP from 2002 to 2018 was estimated based on the MuSyQ-NPP model and the estimated time series LAI with 30 m resolution, and the 30 m resolution forest disturbance time series product was used to analyze its impact on the NPP in the growth season. The results shown that in the north section of the Greater Khingan Mountain, the multi-year average of NPP is mostly distributed from 400 to 600 g C · m-2 · a-1, and shown a slow increasing trend on the whole. Fire disturbance may be the main disturbance factor in the study area. From 2002 to 2017, the annual average reduction of NPP caused by forest disturbance was 0.01 Tg C · a-1. In 2003, 2006 and 2017, NPP decreased significantly in the forest growth season after large area and high intensity disturbances, the reduced values were 0.11 Tg C, 0.03 Tg C and 0.03 Tg C, respectively. The NPP in the disturbed forest area in 2006 with medium and high intensity disturbance was higher than that in the year before the disturbed year since 2009. In conclusion, the disturbance with large area and high intensity has great effects on NPP, and the NPP in the medium-high intensity disturbance area recovers rapidly.

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A Review of the Application of Multi-objective Algorithms in Satellite Regional Coverage Scheduling and Data Transmission Planning
Qi'en HE,Feng LI,Xing ZHONG
Remote Sensing Technology and Application    2023, 38 (4): 783-793.   DOI: 10.11873/j.issn.1004-0323.2023.4.0783
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With the continuous development of the aerospace industry around the world, the satellite imaging business has developed towards the goal of multi-satellite collaboration covering large areas. In this process, multiple objective functions such as maximum coverage area and minimum satellite resource utilization need to be optimized simultaneously. Focusing on the whole process of regional coverage scheduling and data transmission planning of Earth observation satellites, the typical regional decomposition technology is firstly summarized, which plays an important role in satellite scheduling as a preparatory step for satellite regional coverage and makes the solving of combinatorial optimization problems possible. Then, the representative studies of Multi-Objective Evolutionary Algorithm (MOEA) in the field of multi-satellite joint regional coverage scheduling and data transmission planning in recent years are analyzed and reviewed. Common optimization goals include maximizing coverage rate, minimizing overlap ratio, minimizing the number of strips and so on. Finally, we summarize and put forward some prospects for future research, to provide a reliable reference for the application of multi-objective algorithms in related tasks.

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Remote Sensing Monitoring of Yield Loss of Multiple Cropping Paddy Caused by Low Temperature
Wendong QI,Liming HE,Anpeng WANG,Xiaohe GU,Yanbing ZHOU
Remote Sensing Technology and Application    2023, 38 (3): 558-565.   DOI: 10.11873/j.issn.1004-0323.2023.3.0558
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The low temperature during the growth period of paddy will greatly reduce the grain yield. Remote sensing monitoring of yield loss under low temperature stress is of great significance for variety improvement, field management and agricultural insurance claims. The study aimed to monitor yield loss of multiple cropping paddy using multi-temporal remote sensing images. with the support of the field samples, the model of monitoring yield loss of multi-cropping paddy was developed. The results showed that the growth period of paddy in which low temperature injury occurred was different,and the effect on rice yield was quite different.The effect of cold injury on middle rice in the middle filling stage is relatively small, with an average yield of about 6 637 kg/ha, with a yield reduction of nearly 20%. The yield of early late rice is significantly lower than that of middle rice after suffering from low temperature and cold injury at heading stage, with an average yield of 4 143 kg/ha and a yield reduction of about 45%. The yield of late-maturing late rice was most affected by continuous low temperature at jointing stage, with an average yield of only 1 541 kg/ha, which was much lower than that of previous years. The regression model was constructed by using the actual cut sample yield data and Sentinel data in several key phenological periods (NDVI). The R2 was more than 0.75. the precision was cross-verified by the measured sample yield data, and the MAPE was less than 10%. With the help of a small amount of ground data, this method can accurately calculate the yield of multi-cropping rice under the condition of low temperature and cold injury, which provides a new idea for the calculation of rice yield under complex conditions.

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Algorithm and Application of Modified Film-Based & Class-Oriented for Bamboo Forest Information Remote Sensing Extraction
Zhanghua XU,Yiwei ZHANG,Zenglu LI,Songyang XIANG,Qi ZHANG,Yifan LI,Xin ZHOU,Hui YU,Wanling SHEN
Remote Sensing Technology and Application    2023, 38 (2): 393-404.   DOI: 10.11873/j.issn.1004-0323.2023.2.0393
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Refined remote sensing identification of bamboo forests in complex terrain areas can help understand the temporal distribution of bamboo forests and incorporate the ecological, economic, and social values of bamboo forests. In-depth analysis and the effective use of spectral differences and textural features in bright and shadow areas are key issues of the refined identification of bamboo forest information. In this study, we modified the “Film-Based & Class-Oriented” (FB-CO) algorithm and verified the effectiveness of the improvement using Sentinel-2A MSI images. In the “Modified Film-Based & Class-Oriented” (MFB-CO) bamboo forest information remote sensing extraction algorithm, the normalized shaded vegetation index (NSVI) is used instead of single-band thresholds to segment the forestland in bright and shadow areas, and a linear regression model is utilized to enhance the shadow area information. The BPNN, SVM, and RF classifiers are introduced to extract bamboo forests. The results show that the best segmentation thresholds for forestland in bright and shadow areas based on the NSVI and NIR are 0.41 and 0.23, with an Overall Accuracy (OA) of 96.00% and 83.50%, respectively. After the enhancement of shaded area information, the fitted model R2 was greater than 0.82 for each band, the MRE was less than 5%, the mean value increased for all bands, and the standard deviation decreased. The OA of the bamboo forest extraction is 82.41% for the FB-CO algorithm and 86.51%, 88.43%, and 88.92% for the BPNN, SVM, and RF based on the MFB-CO algorithm, respectively. The latter values are better than those of the FB-CO algorithm. The results show that the MFB-CO algorithm effectively improves the extraction of bamboo forest information by enhancing the implementation of several key steps of the FB-CO algorithm, providing technical support for the refinement of bamboo forest identification.

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A Comparative Study on the Extraction of Phenological Parameters in Chongqing Area based on Different Vegetation Indices
Yunlong LI,Jun LI,Ziyu CHANG
Remote Sensing Technology and Application    2023, 38 (4): 978-989.   DOI: 10.11873/j.issn.1004-0323.2023.4.0978
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It is an important basis of analyzing long-term phenological changes that extracting phenological parameters based on different vegetation indices. Takes the cloudy and foggy area, Chongqing, as an example. Three long-term vegetation index data of NDVI, EVI, and EVI2 are extracted based on MODIS remote sensing images from 2010 to 2019, and the characteristics of different vegetation indexes are analyzed through D-L filtering. The results, which is of phenological parameters extracted based on three vegetation indices, were studied using dynamic threshold method and trend analysis method, and their response relationships and differences to topographic factors are compared. The results are as follows: ①The time series fitting curve of EVI and EVI2 is smoother than the fitting curve of NDVI. The differences between the original values of the three vegetation indices and the fitted values are mainly distributed in NDVI (0.05~0.18), EVI (0.03~0.11), EVI2 ( 0.03~0.1). ②The spatial distribution and change trend of the phenological parameters extracted from the three plantations were consistent. The vegetation index parameters extracted from EVI and EVI2 were similar, accounting for more than 79% within 5 days, and the significant change area of SOSEVI2 was the highest (16.36%), while the lowest SOSNDVI was 12.37%.③SOS was delayed with the increase of altitude, EOS was delayed and then advanced with the increase of altitude, LOS was extended and then shortened with the increase of altitude,and EOSNDVI and LOSNDVI were significantly different from EOSEVI/EOSEVI2 and LOSEVI/LOSEVI2 with the increase of altitude, respectively. The phenological parameters extracted by EVI were similar to those of EVI2, and the variation trend was consistent. The phenological parameters can be better extracted based on EVI/EVI2 in cloud and fog areas, and the results are similar and can be used interchangeably. The phenological parameters extracted based on EVI and EVI2 have more obvious differences in altitude, slope, and slope direction.

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Research on Data Acquisition Technology of Chinese High-resolution Broadband Multispectral Satellites in the “The Belt and Road Initiative” Region
Songyan WEI,Xiangqiang MENG,Xiaobin YI,Feng LI,Xing ZHONG,Si CHEN
Remote Sensing Technology and Application    2023, 38 (4): 776-782.   DOI: 10.11873/j.issn.1004-0323.2023.4.0776
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Rapid acquisition of large area data is an important research topic in the field of remote sensing satellite task planning. Relying on the project of "Data Cube for large coverage datasets of Chinese high resolution and broadband and multispectral satellite constellation",Jilin-1GP01/02 satellite was used to carry out effective coverage of 65 countries and regions along the "The Belt and Road Initiative" twice within three years.This paper summarizes the strategy, methods and experience of data acquisition in large areas of the project, and focuses on various influencing factors such as the resource of satellites and ground stations related to data acquisition,the strategy of dividing large areas in time and phase, and the dynamic planning process of large areas based on effective imaging strips of cloud forecast, that is,within the single transit range of the satellite,,select the imaging strips with the maximum probability of obtaining effective data in combination with the cloud map. The research has provided normalization support for the project,and the relevant methods and project experience can provide reference for the general satellite remote sensing large-scale and wide-area data acquisition tasks.

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