Data Articles
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- Abstract：Near-surface soil freeze/thaw (F/T) state is an important variable in water cycle and carbon cycle systems. Accurately obtaining the F/T state of near-surface soil and its spatial and temporal changes is important for research on hydrological processes, climate change, and ecology. The main existing F/T products based on passive microwave remote sensing data are unstable in large scale with relatively complex topography, climate, and soil conditions, and the accuracy has yet to meet the requirements of applications. The microwave radiation imager (MWRI) carried on China’s FY-3 satellite can acquire passive microwave remote sensing data and is currently less used in near-surface soil F/T monitoring. To address the problems of the existing near-surface soil F/T products, this study presented the near-surface soil F/T dataset of China from 2010 to 2021 based on the FY-3/MWRI data.Method The algorithms used to obtain the dataset consist of a primary and a secondary algorithm, i.e., the dynamic near-surface soil F/T detection algorithm and the seasonal threshold algorithm, respectively. The dynamic F/T detection algorithm is developed based on the union of soil F/T discriminant algorithm and edge detection algorithm and performs stably at large scales. To avoid evident F/T misclassification, corrected ERA5-Land temperature data were first used to identify areas that are not subject to F/T cycles before generating the near-surface soil F/T dataset. To reduce the effect of precipitation and water bodies on the accuracy of F/T dataset, precipitation is labeled using GPM precipitation data, and water bodies are labeled using land cover data (GlobeLand30-2010).Finally, the daily near-surface FY-3B (2010—2019) and FY-3D (2017—2021) F/T datasets consisting of daytime (ascending orbit) and nighttime (descending orbit) are presented. The in situ 5 cm soil temperature data obtained from the Qinghai-Tibetan Plateau, the Genhe watershed in Northeastern China, and the Saihanba area in Northern China were used to evaluate the FY-3B and FY-3D F/T datasets. The accuracy of the near-surface soil F/T dataset presented in this study is stable across seasons and climate zones and performs best when compared with the other existing passive microwave remote sensing F/T products. The overall accuracy of the presented F/T dataset is more than 86%.By analyzing the spatial and temporal variations of near-surface soil F/T from 2011 to 2020 based on the presented dataset, we found that the annual thaw onset was delaying, the annual frozen onset was advancing, and the annual frozen days was increasing during the 10-year period over the Qinghai-Tibetan Plateau, whereas no considerable change was observed over other regions. The vegetation Net Primary Productivity (NPP) and Gross Primary Productivity (GPP) were negatively correlated with the land surface annual thaw onset date and annual frozen days, with the coefficient of determination ranging from 0.52 to 0.72. The earlier the date of land surface thawing and the fewer the annual frozen days, the higher the annual NPP/GPP. These analyses demonstrated the potential application of this presented F/T dataset in studies of climate change, vegetation biomass, and vegetation carbon stocks. The dataset is stored in H5 file format and can be downloaded at DOI:10.11888/Crvos.tpdc.300445.Keywords：near-surface soil freeze/thaw status datasets;FY-3B;FY-3D;microwave radiation imager;dynamic near-surface soil freeze/thaw detection algorithm1055|1895|0
<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-01-05 - Abstract：High-standard farmland construction is a crucial initiative in enhancing grain production capacity and ensuring food security in China. Rural roads, including field paths and production routes, are the core components of the infrastructure for high-standard farmland construction. The rapid and accurate extraction of rural roads from high-resolution satellite imagery is crucial to monitoring and regulating high-standard farmland construction.We systematically constructed the first benchmark dataset for rural (high-standard farmland) road extraction from high-resolution satellite imagery (WHU-RuR) in this study. This dataset collected high-resolution satellite images covering typical rural areas in seven provinces (Hubei, Hunan, Shaanxi, Sichuan, Anhui, Henan, and Hebei) in different regions of China. These images were uniformly annotated manually. The training set and the test set consisted of 25922 pairs of high-resolution satellite images and road samples, with a spatial resolution of 0.3 m and a spatial size of 1024×1024 pixels. At the same time, experiments were conducted using state-of-the-art deep learning methods in the road extraction task.The constructed WHU-RuR is currently an open-source dataset with the highest farmland coverage, the richest rural road categories, the most complex rural background, and the largest amount of data. However, the current deep learning road extraction method still has the problem of poor extraction effect for rural roads. In future research, the rural road extraction method based on the WHU-RuR dataset can conduct in-depth research on the unique challenges of large intraclass differences in rural roads, maintenance of road connectivity, foreground-background category imbalance, and foreground-background spectral similarity.This study constructed the first high-resolution remote sensing image of a rural (high-standard farmland) road extraction benchmark dataset (WHU-RuR). To verify the usability of the WHU-RuR dataset, this study tests and comprehensively analyzes the effect of deep-learning road extraction methods on rural road extraction tasks. Results indicated that the WHU-RuR dataset satisfies the basic requirements for rural road extraction and has remarkable potential applications in the monitoring and regulation field of high-standard farmland. The dataset can be accessed at https://doi.org/10.57760/sciencedb.09181.Keywords：Rural road extraction;high-standard farmland;high spatial resolution remote sensing;benchmark dataset of remote sensing;deep learning2098|2229|4<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-01-05
- Abstract：Building information extraction from remote sensing images plays an essential role in urban information management and disaster prevention and mitigation. This study establishes a fine-grained building feature set, namely, MFBFS, for high-resolution multispectral remote sensing images. MFBFS uses the domestically produced Gaofen-2 multispectral remote sensing images as data source and selects 21 districts and counties with concentrated buildings in various disaster zones in China, covering 3668 km2 as the study area. These regions include Yongjia County, Xuwen County, and Wanning City in the southeastern coastal disaster belt; Ning'an City, Kaiyuan City, Laiyuan County, Shouguang City, Xinxiang County, Lujiang County, Hengdong County, and Songbei District in the eastern disaster belt; Daning County, Enshi City, Tengchong City, and Shuicheng County in the central disaster belt; Kashgar City, Yizhou District, and Pingluo County in the northwest disaster belt; and Diebu County, Yushu City, and Milin County in the Qinghai–Tibetan disaster belt. To obtain high-quality and high-resolution remote sensing images, a series of preprocessing procedures was applied to the Gaofen-2 images. Initially, poor-quality images were removed, followed by radiometric and orthorectification corrections on multispectral and panchromatic images, respectively. Finally, the panchromatic images were fused to enhance the spatial resolution of the multispectral images, resulting in a spatial resolution of 0.8 m. Seventeen feature components were generated from four perspectives: spectral, texture, edge, and index. Spectral features include features from the blue, green, red, and near-infrared bands. Texture features consist of contrast, dissimilarity, homogeneity, correlation, angular second moment, local binary pattern, and histogram of oriented gradients. Edge features comprise first-order and multi-order edge characteristics. Index features include building, shadow, vegetation, and water indexes. MFBFS encompasses over 260000 building instances, ensuring high intra-class diversity in terms of size, shape, color, orientation, background, and structural type. These instances are classified into three structural types, namely, steel and reinforced concrete, masonry, and block stone structures, significantly reflecting the abilities of buildings to resist disasters and their usable lifespans. The fine-grained design will cause the task of extracting buildings through remote sensing to play a greater role, particularly in pre-disaster loss prediction and post-disaster loss assessment in the disaster field. Rigorous quality control processes and field inspections were conducted to ensure the high accuracy of ground truth values. This procedure involved adherence to interpretation standards and inviting data inspectors and remote sensing image experts to assess the quality of remote sensing images and corresponding ground truth values. Ultimately, 191 GB of high-quality feature and label data were obtained. Each of the 17 feature components comprises 11005 512×512-sized feature maps with a spatial resolution of 0.8 m, uniformly expanded to a value range of [0,1]. Initial deep learning experiments demonstrate the effectiveness of MFBFS. This feature set, available for download athttps://github.com/WangZhenqing-RS/MFBFS, provides robust data support for fine-grained building structure extraction research and promotes the development of domestic high-resolution remote sensing data applications.Keywords：High-resolution remote sensing;multispectral imagery;fine-grained categories;building extraction;feature sets1599|1898|1<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-12-30
- Abstract：Reclamation is an important cause of coastline changes, coastal wetland degradation, and offshore marine pollution. The control and management of reclamation are related to the protection of the national coastal zone and the construction of an ecological civilization. At present, high-frequency remote sensing monitoring of national-scale coastal reclamation types and their spatial distribution is lacking, and the tracking and monitoring of reclamation management measures based on remote sensing methods have not yet been effectively performed. By using the method of integrated remote sensing dynamic monitoring of the coastline and reclamation, and based on Landsat time-series satellite images, the spatial distributions of China’s coastal reclamation in the periods of 2010—2015, 2015—2018, and 2018—2020, and the spatial distributions of the measures of returning enclosures to the sea and wetland in the corresponding periods were extracted. The data outcomes were stored in the ArcGIS Shapefile format, and the compressed data volumes totaled 680 KB. The remote sensing dynamic monitoring of national-scale time-series coastal reclamation that is synchronized with the time of coastline change and shared the same satellite image basis is highly significant for the protection of coastline resources and the evaluation of the effect of coastal ecological and environmental management. The results showed that during the second decade of the 21st century, the area of newly coastal reclamation in China declined sharply and that the growth rate of coastal reclamation was effectively controlled. Meanwhile, the rate of measures for returning enclosures to the sea and wetland, which was mostly manifested as the restoration of aquaculture pits and ponds to mudflats and sea surfaces, was increased abruptly, particularly in 2018—2020. Achievements were directly related to the unprecedented strengthening of national policies for reclamation control and coastal zone protection in 2018. The remote sensing monitoring dataset of coastal reclamation dynamics can provide basic data guarantee for national ocean and coastal zone management and scientific research, and important support for the realization of Sustainable Development Goal 14.5.Keywords：remote sensing;coastal reclamation;dynamic;coastal zone;China1173|1984|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-12-30
- Abstract：With the development of Synthetic Aperture Radar (SAR) imaging and deep learning, the use of deep learning to classify land cover in SAR images has received extensive attention and applied research. In this study, a high-resolution airborne multidimensional SAR land cover classification dataset is constructed on the basis of the high-resolution airborne data of the Chinese Aeronautic Remote Sensing System (CARSS) for Earth observation, namely, AIR-MDSAR-Map (Airborne Multidimensional Synthetic Aperture Radar Mapping Dataset).The original data are obtained by CARSS, and the platform is a modified Xinzhou 60 remote sensing aircraft. SAR and optical images are generated in accordance with the standard data production process. After imaging processing, radiometric correction, polarization correction, and geometric correction, the original SAR data are preprocessed to form Single Look Complex (SLC) data, and then geometric processing is used to generate SAR DOM products. After image enhancement, splicing, and rough correction, the raw optical data are preprocessed to generate DSM data, and then semiautomatic filtering is performed to produce DEM. Finally, AIR-MDSAR-Map contains polarization SAR images in bands of C, Ka, L, P, and S and high-resolution optical images in Wanning, Hainan, and Sheyang, Jiangsu, with the spatial resolution ranging from 0.2 m to 1 m depending on the band.AIR-MDSAR-Map divides the land cover into nine categories and generates fine pixel-level labels through a semiautomatic labeling algorithm. In this study, the classical semantic segmentation methods in deep learning, such as UNet, SegNet, DeepLab, and HRNet, are used to verify the classification of AIR-MDSAR-Map. At the same time, we test the classification sensitivity of different band images to all kinds of land cover objects.This dataset includes multidimensional SAR images of the same place and time, which can be used for fusion classification research. In this study, multidimensional SAR data are fused and classified through different fusion strategies; model fusion classifies land cover by selectively fusing the models of each band, and the a priori fusion uses the prior information of the classification results in each band to distinguish land cover on defining the priority of objects. These two fusion methods outperform the single band in the performance of some types of land cover and improve the FWIoU and PA by 10%—15%, the FWIoU reaches 69%, and PA is 81%.AIR-MDSAR-Map can satisfy the research and application requirements of different users and can be used to study the characteristics of the same land cover object with different resolutions, bands, and polarizations. Moreover, it can provide a strong promotion for the development of multidimensional SAR applications. The AIR-MDSAR-Map will be available at the ChinaGEOSS Data Sharing Network (http://www.chinageoss.cn).Keywords：remote sensing;airborne SAR;multi-dimensional;land cover classification;deep learning;semantic segmentation;AIR-MDSAR-Map2399|4188|1<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-11-06
- Abstract：Phytoplankton are indispensable part of the marine ecological environment, and their size class PSC (Phytoplankton Size Class) is a key parameter to describe the vital role of phytoplankton in different geobiochemical cycles. The Yellow Sea, the Bohai Sea and the East China Sea are located in the eastern part of China as a whole, shown as semi-closed characteristics. The PSC field measurement is mainly dependent on the in situ cruise observation experiments carried out in recent years. The sampling points are sparse and uneven in space and time. Therefore, it is necessary to use high Remote sensing inversion technology with a wide range of frequency and coverage to fill the insufficiency of field measured data.Based on the sea surface remote sensing reflectance products of MODIS/Aqua sensors from 2002-08 to 2022-05, this paper applies the PSC remote sensing inversion model constructed by Sun et al. (2019) to produce PSC long-term data set. The data set is stored in the standard format of Matlab and contains 238 files in total, which are easy to read by each software (DOI: 10.17632/mjg5s9p4wp.3). The product accuracy verification results show that the satellite inversion and the field measurement results are relatively consistent (the average absolute percentage error is 22.9%, 11.4%, and 35.0% for micro, nano, and picophytoplankton, respectively). At the same time, the comparison of spatial distribution in different sea areas shows that the PSC inversion after reconstructing the chlorophyll a concentration is closer to the field measured value.The statistical results of the long-term distribution of PSCs based on this dataset show that the coastal waters are mainly enriched by microphytoplankton, while the offshore waters are primarily dominated by nanophytoplankton. Judging from the multi-year monthly averaged PSCs in five specific areas, taking microphytoplankton as an example, there are “double peaks” in spring (May) and summer (July) in the center of the Bohai Sea and the mouth of the Yangtze River, while the North Yellow Sea area presents a spring (April), autumn (October) peak feature. Meanwhile, the spring peaks in the offshore waters of the South Yellow Sea and the East China Sea are more significant in April and March, respectively.This dataset is helpful for fine-grained analysis and understanding of the temporal and spatial variation of phytoplankton in the Yellow Sea, the Bohai Sea, and the East China Sea. It can also be used as a routine project for water environment monitoring and is worthy of popularization.Keywords：Remote sensing dataset;phytoplankton size class;Bohai Sea;Yellow Sea and East China Sea2037|2304|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-07-19
- Abstract：Studying marine phytoplankton communities is essential for understanding the carbon cycle and climate change. Phytoplankton pigments can describe the composition and physiological state of phytoplankton communities. Detecting phytoplankton pigment concentrations is also important, and remote sensing technology permits macroscopic long-term series monitoring of phytoplankton pigment concentrations. However, existing studies still have limitations. First, remote sensing methods for retrieving additional types of pigments are lacking. Existing studies have focused primarily on a few pigments or pigment groups. Second, the existing pigment inversion algorithms are mostly based on oceanic water data, and studies of optical class II waters off China are insufficient. Finally, satellite remote sensing datasets for long time series of multiple phytoplankton pigment concentrations in phytoplankton-related fields are lacking, indicating low data support. In this study, phytoplankton absorption data, 16 pigment concentration data points, and remote sensing reflectance data were collected. A total of 7 cruise experiments were performed in the Bohai Sea, Yellow Sea, and East China Sea from 2016 to 2018. Then, a remote-sensing model and a long-term series dataset of the spatiotemporal distribution of phytoplankton pigment concentrations were developed.The remote removal of fine particulate matter was achieved by determining the relationship between phytoplankton absorption and the 16 pigments. The measured absorption coefficients were decomposed into Gaussian functions, and the relationship between the Gaussian parameters and the measured pigment concentration was analyzed to construct inversion models. A two-component model of phytoplankton size classes was also used to determine hyperspectral phytoplankton absorption. The performance of the models was evaluated for consistency. Then, the models were assessed using in situ datasets and leave-one-out cross-validation methods. The results showed competitive and acceptable error results, with Mean Absolute Percentage Errors (MAPEs) of less than ~60% for most pigments. Satellite-measured validation also produced promising prediction errors, yielding MAPEs in the range of 40%—60% for most pigments. Finally, the developed models were applied to the SeaWiFS and MODIS-Aqua remote sensing reflectance monthly mean products (1998—2020) to obtain 23 years of spatiotemporal patterns of 16 pigment concentrations in the Bohai Sea, Yellow Sea, and East China Sea.The satellite remote sensing dataset revealed 16 similar pigment distribution patterns, revealing a decreasing trend from nearshore to offshore waters. In the Bohai Sea, the pigment concentration is high in winter and spring and low in summer. In summer, the pigment concentration peaks in the coastal areas of Jiangsu Province and gradually decreases toward Zhejiang and Fujian Provinces. A triangular high concentration is apparent in the Yangtze River Estuary, with the area extending from west to east in autumn and winter. The phytoplankton pigment concentration was relatively low in the outer deepwater area, and the variation in concentration with season was only slight.The remote sensing datasets of 16 phytoplankton pigment concentrations can be downloaded fromhttps://doi.org/10.17632/bhcznf2m7v.1. In related fields, scholars can study the macroscopic and continuous phytoplankton community structure monitoring and physiological characteristics of phytoplankton in the Bohai Sea, Yellow Sea, and East China Sea based on information from pigment concentration remote sensing datasets. This dataset can enrich the understanding of marine phytoplankton pigment distributions and provide data support for satellite-based detection of phytoplankton community composition.Keywords：Phytoplankton pigments;absorption coefficient;Coastal water;SeaWiFS;MODIS;Remote sensing dataset2045|2565|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-05-15
- Abstract：Land-Use Scene Classification and change Detection (LUSCD) aim to recognize land-use types and monitor their changes by using Remote-Sensing (RS) images, which play an important role in urban planning and land-use optimization. In the era of RS big data, conventional hand-crafted feature-based methods are infeasible for LUSCD because the extracted features are not sufficiently discriminative for RS images with high complexity. As a novel data-driven paradigm for information extraction from RS images, deep learning provides a new solution for LUSCD. However, the existing publicly available datasets have limited samples and is thus unable to train a successful deep-learning model. Therefore, it has great significance in constructing an open and large-scale LUSCD benchmark.To advance the progress of LUSCD using deep-learning methods, this paper releases a large-scale scene classification and change-detection dataset termed Multi-temporal Scene Classification and Change Detection (MtSCCD). The RGB images in MtSCCD are cropped from large-size high-resolution RS images captured from the central areas of five China cities, namely, Hangzhou, Shanghai, Wuhan, Nanjing, and Hefei. The size of the cropped images is 300×300 pixels with the spatial resolution of around 1 m. MtSCCD has 10 land use classes, which are residential land, public service and commercial land, educational land, industrial land, transportation land, agricultural land, water body, green space, woodland, and woodland. Based on the cropped land-use images in MtSCCD, this paper constructs two sub-datasets termed MtSCCD_LUSC (MtSCCD Land Use Scene Classification) and MtSCCD_LUCD (MtSCCD Land Use Change Detection) for land-use scene classification (LUSC) and land-use change detection (LUCD), respectively. MtSCCD dataset has the following characteristics. (1) It is currently the largest publicly available LUSCD dataset, and both of the two sub-datasets (i.e., MtSCCD_LUSC and MtSCCD_LUCD) have 65548 images in total. (2) The images in MtSCCD are split into training set, validation set, and testing set according to the five cities. For example, images from three of the five cities are randomly split into training and validation set, whereas the rest remain to be the testing set. Therefore, MtSCCD has high extensibility, i.e., it can be easily extended to be a larger dataset. (3) For a deep-learning model, the training set and testing set are categorized from different cities, so it is beneficial to demonstrate the model’s generalization ability. (4) MtSCCD has high intra-class diversity, making it a challenging dataset.Based on MtSCCD_LUSC and MtSCCD_LUCD, this paper evaluates several deep-learning feature-based methods for LUSC and LUCD. Specifically, AlexNet, VGG networks (i.e., VGG16 and VGG19), GoogLeNet, and ResNet networks (i.e., ResNet18, ResNet50, and ResNet101) are selected to extract deep-learning features that are then fed into SVM for LUSC. We also evaluate DenseNet, EfficientNet, SENet, ViT, and SwinT for LUSC. Two kinds of LUCD approaches including conventional classification-based methods and current similarity-based methods have been evaluated. Experimental results show that the highest overall accuracy of MtSCCD_LUSC dataset is around 76%, indicating much room for improvement. Regarding LUCD, similarity-based methods particularly similarity learning-based ones outperform classification-based methods by a significant margin, providing a promising research direction for LUCD.This paper presents the currently largest scene classification and change-detection dataset MtSCCD based on high-resolution RS images of the central area of five China cities. MtSCCD contains two subsets MtSCCD_LUSC and MtSCCD_LUCD. Both had 10 land-use types and 65548 images in total. Based on the two sub-datasets, this paper evaluates the performance of several deep networks for scene classification and change detection, expecting to provide baseline results for related researchers. We hope that the MtSCCD dataset can promote this progress in land-use type recognition and monitoring.Keywords：land use;scene classification;change detection;dataset;information extraction;feature extraction;deep learning;convolutional neural network4674|3784|6<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-03-22
- Abstract：Sample collection is one of key research foundations for wetland mapping. It plays an important role in classifier training and accuracy validation. Generally, wetland samples are produced by visual interpretation based on high-spatial-resolution images or automatic generation based on multi-source existing dataset. The visual interpretation is time and labor consuming and cannot meet the demand for large-scale wetland classification. The automatic sample-generation method is unsuitable to detailed-type wetland mapping due to the diversity of wetlands and classification-scheme inconsistency of existing wetland datasets. Thus, an efficient and accurate sampling method is in demand for large-scale and detailed-type wetland mapping.In our study, we collected a series of auxiliary datasets and developed an efficient solution for continental-scale wetland sample generation by combining automatic sampling method and visual interpretation. In the first part, the samples of five wetland types can be automatically generated by rule filtering based on multi-source existing datasets. River, lake, and reservoir samples were created using the JRC Global Surface Water, Global River Widths from Landsat and HydroLAKES datasets. Coastal swamp (mangrove) samples were produced by using Global Mangrove Watch dataset. Tidal flat samples were generated using the Global Intertidal Change dataset. In the second part, by combining time series of MODIS NDVI images and existing auxiliary datasets, we first produced potential wetland samples for coarse wetland types (i.e., vegetated wetland samples and inundated wetland samples). Then, we identified them by visual interpretation based on the Google Earth Engine platform, Google Earth software, and Collect Earth software. We applied our sample method in our study area, and produced continental-scale and detailed-type wetland samples.Results indicated that the total wetland samples in our study area was 150688, among which 141412 points were inland wetland samples, 11563 were coastal wetland samples, and 17693 were human-made wetland samples. Among the 13 wetland sub-categories, lake accounted for the largest proportion (39.22%) and primarily distributed the northern and central of study area, whereas lagoon accounted for the smallest proportion (0.19%), mostly scattered in coastal region of the study area. Samples of river, reservoir, inland swamp, and inland marsh also shared a considerable amount, accounting for 16.93%, 9.86%, 7.16%, and 11.12% of total wetland samples, respectively. River samples were primarily distributed north and south of the study area, and reservoir samples were primarily scattered south of the study area. Meanwhile, inland swamp and inland marsh samples were mostly distributed northwest and south of the study area.This study successfully produced stable and high-quality wetland samples at continental scale. The generated samples shared sufficient quantities and reasonable spatial distribution, which can lay a good foundation for classifier training and accuracy validation. Meanwhile, by combining the multi-source thematic datasets and multiple platform, our designed sample solution can make full use of the existing database and greatly reduce manual workload. It can also create high-quality samples for complex wetland types, such marsh, swamp and floodplain. Overall, the designed sample method in our study was efficient and reliable, which has significance for large-scale wetland mapping.Keywords：remote sensing;wetland;sample production;multi-source thematic data;visual interpretation2289|3005|3<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-03-22
- Abstract：Flood disasters are a great threat to the national economy and people’s property along the lakes and rivers in China. Synthetic Aperture Radar (SAR) adopts active imaging methods that can realize all-weather imaging and ensure continuous observation of flood disaster areas under severe weather, such as heavy rains and clouds. The current flood-monitoring methods based on SAR images often have problems, such as difficulty in threshold selection, high computational cost, or inefficient use of time-series information. Aiming at the above problems, this paper makes full use of information from time-series SAR image sequence to design an effective and stable method of monitoring flood, which can be adapted to complex areas.Through preprocessing and statistical analysis of the image sequence, two normalized difference indices including submerged range extraction index and submerged range in vegetation area extraction index are designed and applied to calculate the candidate area of flood inundation. Then, the adaptive selection method of threshold for flood extraction is given based on the stability assumption of vegetation seasonal distribution in the same area. Finally, considering the characteristics of the surrounding features of the lakes in China, a post-processing process is designed. The process involves removing spots and holes, excluding areas with large slopes and filtering out fragmented areas with large rectangular degrees. Post-processing is conducted to optimize the extraction area for the final results of flood-inundation range.In the experiment, this paper takes the East Dongting Lake basin as the main research area to verify the effectiveness of the proposed method by comparing the extraction accuracy with the other four methods. Experimental results prove that the overall extraction accuracy of the proposed method is higher than that of all comparative methods. To achieve the purpose of flood-disaster monitoring and evaluation, an analysis of the flood-disaster situation in the East Dongting Lake Basin in 2020 and an analysis of flood submerged land cover types are conducted. The method has also been successfully applied to the data of the East Dongting Lake basin in previous years and the Poyang Lake basin in that the method can be applied across time and space.Based on the time-series SAR image sequence, this paper proposes an effective method, forms a detailed process flow, and constructs a general framework for flood monitoring. The proposed method has advantages of simple parameter setting and low user dependence on threshold determination. Experiments show that the method has high extraction accuracy of submerged areas with good robustness and versatility. The proposed method can be applied to different flood-monitoring scenarios across time and space and can preliminarily distinguish different attributes of submerged areas. Thus, a certain reference is provided for flood-disaster monitoring, assessment, and early warning in other regions.Keywords：Flood disaster;synthetic aperture radar;time series monitoring;hydrological remote sensing;SAR;Dongting Lake5822|5519|11<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-03-22
