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Semantic segmentation for remote sensing images based on an AD-HRNet model

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International Journal of Digital Earth 2022年第1期15卷 2376-2399页

作者： Xue Yang Xiang Fan Mingjun Peng Qingfeng Guan Luliang Tang School of Geography and Information Engineering China University of GeosciencesWuhanPeople’s Republic of China Wuhan Geomatics Institute WuhanPeople’s Republic of China State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanPeople’s Republic of China

Semantic segmentation for remote sensing images faces challenges of unbalanced category weight,rich context causing difficulties of recognition,blurred boundaries of multi-scale objects,and so on.To address these problems,we propose a new model by combining HRNet with attention mechanisms and dilated convolution,denoted as:AD-HRNet for the semantic segmentation of remote sensing images.In the framework of AD-HRNet,we obtained the weight value of each category based on an improved weighted cross-entropy function by introducing the median frequency balance method to solve the issue of class weight imbalance.The Shuffle-CBAM module with channel attention and spatial attention in AD-HRNet framework was applied to extract more global context information of images through slightly increasing the amount of computation.To address the problem of blurred boundaries caused by multi-scale object segmentation and edge segmentation,we developed an MDC-DUC module in AD-HRNet framework to capture the context information of multi-scale objects and the edge information of many irregular objects.Taking Postdam,Vaihingen,and SAMA-VTOL datasets as materials,we verified the performance of AD-HRNet by comparing with eight typical semantic segmentation models.Experimental results shown that AD-HRNet increases the mIoUs to 75.59%and 71.58%based on the Postdam and Vaihingen datasets,respectively.

关键词： Semantic segmentation convolutional neural networks dilated convolution attention mechanism remote sensing

Coupling remote sensing and crop growth model to estimate national wheat yield in Ethiopia

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Big Earth Data 2022年第1期6卷 18-35页

作者： Awetahegn Niguse Beyene Hongwei Zeng Bingfang Wu Liang Zhu Tesfay Gebretsadkan Gebremicael Miao Zhang Temesgen Bezabh State Key Laboratory of remote sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina College of Resources and Environment University of Chinese Academy of SciencesBeijingChina Tigray Agricultural Research Institute MekelleEthiopia Climate Change and Rural Development Mekelle UniversityMekelleEthiopia

Estimation of crop yield at a regional level is essential for making agricultural planning and addressing food security issues in Ethiopia.remote sensing observations,particularly the leaf area index(LAI),have a strong relationship with crop yield.This study has proposed an approach to estimate wheat yield at field level and regional scale in Ethiopia by assimilating the retrieved MODIS time-series LAI data into the WOrld FOod STudies(WOFOST)model.To improve the estimation of crop yield in the region,the Ensemble Kalman Filter(EnKF)was used to incorporate the LAI into the WOFOST model.The estimation accuracy of wheat crop yield was validated using field-measured yields collected during the 2018 growing season.Our findings indicated that wheat yield was more precisely estimated by WOFOST(at water-limited mode)with EnKF algorithm(R^(2)=0.80 and RMSE=413 kg ha^(−1))compared to that of without assimilating remotely sensed LAI(R^(2)=0.58,RMSE=592 kg ha^(−1)).These results demonstrated that assimilating MODIS-LAI into WOFOST has high potential and practicality to give a reference for wheat yield estimation.The findings from this study can provide information to policy,decision-makers,and other similar sectors to implement an appropriate and timely yield estimation measure.

关键词： Wheat yield WOFOST model Ensemble Kalman Filter remote sensing

Assessing factors impacting inter-satellite variability of grassland curing estimates for fire monitoring in Victoria, Australia using remote sensing

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International Journal of Digital Earth 2023年第1期16卷 3368-3383页

作者： Sike Li School of Science University of New South Wales Canberra at the Australian Defence Force AcademyCampbellAustralia

Grassland fires in Victoria,Australia pose significant environmental issues.Monitoring these fres relies on the Grassland Curing Degree(GCD)indicator and the corresponding Grassland Curing Map(GCM)for spatial distribution.Inter-satellite variability(IsV)assesses the variations in Grassland Curing Maps(GCMs)produced from remote sensing data with varying spatial resolutions(SR).Higher SR data improves GCM accuracy but increases processing time.IsV helps identify priority areas that need higher SR imageries.This study analyzes sample sites in Victoria and finds correlations between Isv,seasonality,temperature,precipitation,and distance to residential areas.Results reveal lower Isv during summers and autumns compared to winters and springs.Temperature shows a strong negative linear relationship with ISV,indicating that higher temperatures result in lower ISVv.Precipitation exhibits a weak positive correlation with IsV,suggesting heavier precipitation leads to increased ISV.Distance to grasslands negatively correlates with IsV,indicating that greater distances from residential lands result in lower Isv.Based on these findings,it is recommended to use higher SR satellite data for GCM creation during winters and springs when temperatures are low,precipitation is heavy,and areas are closer to residential lands.Implementation suggestions are provided for fire management based on these results.

关键词： Grassland fire remote sensing environmental monitoring decision support Gis earth observation

Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years'winter wheat yield over the North China Plain

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农业科学学报:英文版 2023年第9期22卷 2865-2881页

作者： ZHANG Sha YANG Shan-shan WANG Jing-wen WU Xi-fang Malak HENCHIRI Tehseen JAVED ZHANG Jia-hua BAI Yun Research Center for Space Information and Big Earth Data College of Computer Science and TechnologyQingdao UniversityQingdao 266071P.R.China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094P.R.China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000P.R.China Department of Environmental Sciences Kohat University of Science and TechnologyKohat 26000Pakistan

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.

关键词： approximating irrigations process-based model remote sensing winter wheat yield North China Plain

Mapping lineaments using Landsat 8 OLI and SRTM data;a case study of the eastern part of the Ouarzazate Basin,Morocco

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Journal of Mountain Science 2024年第3期21卷 987-1003页

作者： MOUJANE Said ALGOUTI Ahmed ALGOUTI Abdellah FARAH Abdelouahed ABOULFARAJ Abdelfatah NAFOURI Imane Geoscience Geotourism Natural Hazards and remote sensing Laboratory(2GRNT) Department of GeologyFaculty of Sciences SemlaliaUniversity of Cadi AyyadBP 239040000MarrakechMorocco

The integration of remote sensing and geographic information system(GIS)was employed in this study to delineate the structural lineaments within the eastern section of the Ouarzazate Basin,situated between the southern front of the Central High Atlas and the northern slopes of the Eastern Anti-Atlas(also known as the Saghro Massif).To achieve this objective,Landsat 8 Operational Land Imager(OLI)and Shuttle Radar Topography Mission(SRTM)data were used.Principal Component Analysis(PCA)was computed and a directional filter was applied to the first PCA and the panchromatic band(B8).Additionally,shading was applied to the SRTM data in four directions;N0°,N45°,N90°,N135°.After removing of the non-geological linear structures,the results obtained,using the automatic extraction method,allowed us to produce a synthetic map that included 1251 lineaments with an average length of 1331 m and was dominated by NE-SW,ENE-WSW and E-W directions,respectively.However,the high lineament density is clearly noted in the Anti-Atlas(Saghro Massif)and at the level of the northern part,extending from the Ait Ibrirne to Arg-Ali Oubourk villages.High lineament density can always be found around the major faults affecting this area.The data collected during the field investigations and from geological maps show that the major direction of the faults and structural accidents range mostly between N45°,N70°and N75°.The correlation of remote sensing results with those collected in the field shows a similarity and coincidence with each other.From these results,it is possible to consider the automatic extraction method as a supplementary kind that can serve classical geology by quickly enriching it with additional data.As shown in this work,this method provides more information when applied in arid areas where the fields are well outcropped.

关键词： remote sensing GIS Landsat 8 OLI SRTM Ouarzazate Basin Central High Atlas

An improved algorithm for retrieving thin sea ice thickness in the Arctic Ocean from SMOS and SMAP L-band radiometer data

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Acta Oceanologica Sinica 2024年第3期43卷 127-138页

作者： Lian He Senwen Huang Fengming Hui Xiao Cheng School of Geospatial Engineering and Science Sun Yat-Sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Key Laboratory of Comprehensive Observation of Polar Environment(Sun Yat-Sen University) Ministry of EducationZhuhai 519082China

The aim of this study was to develop an improved thin sea ice thickness(SIT)retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data.This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort,Chukchi,East Siberian,Laptev and Kara seas and utilized the microwave polarization ratio(PR)at incidence angle of 40°.The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact,reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature.The relationship between the SIT and PR was found to be almost stable across the five selected regions.The SIT retrievals were then compared to other two existing algorithms(i.e.,UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen)and validated against independent SIT data obtained from moored upward looking sonars(ULS)and airborne electromagnetic(EM)induction sensors.The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error(RMSE)being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data.The proposed algorithm can be used for thin sea ice thickness(<1.0 m)estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.

关键词： Arctic sea ice sea ice thickness remote sensing Soil Moisture Active Passive(SMAP) Soil Moisture Ocean Salinity and Soil(SMOS)

Advances in Atmospheric Radiation:Theories,Models,and Their Applications.PartⅡ:Radiative Transfer Models and Related Applications

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Journal of Meteorological Research 2024年第2期38卷 183-208页

作者： Hua ZHANG Feng ZHANG Lei LIU Yuzhi LIU Husi LETU Yuanjian YANG Zhengqiang LI Kun WU Shuai HU Ming LI Tie DAI Fei WANG Zhili WANG Yuxiang LING Yining SHI Chao LIU State Key Laboratory of Severe Weather and Institute of Climate System Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science&TechnologyNanjing 210044 Department of Atmospheric and Oceanic Sciences&Institute of Atmospheric Sciences Fudan UniversityShanghai 200438 College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073 College of Atmospheric Sciences Lanzhou UniversityLanzhou 730000 State Key Laboratory of remote sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100101 School of Atmospheric Physics Nanjing University of Information Science&TechnologyNanjing 210044 School of Atmospheric Sciences Nanjing University of Information Science&TechnologyNanjing 210044 Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029 CMA Earth System Modeling and Prediction Centre China Meteorological Administration(CMA)Beijing 100081

The subject of“atmospheric radiation”includes not only fundamental theories on atmospheric gaseous absorption and the scattering and radiative transfer of particles(molecules,cloud,and aerosols),but also their applications in weather,climate,and atmospheric remote sensing,and is an essential part of the atmospheric sciences.This review includes two parts(Part I and PartⅡ);following the first part on gaseous absorption and particle scattering,this part(PartⅡ)reports the progress that has been made in radiative transfer theories,models,and their common applications,focusing particularly on the contributions from Chinese researchers.The recent achievements on radiative transfer models and methods developed for weather and climate studies and for atmospheric remote sensing are firstly reviewed.Then,the associated applications,such as surface radiation estimation,satellite remote sensing algorithms,radiative parameterization for climate models,and radiative-forcing related climate change studies are summarized,which further reveals the importance of radiative transfer theories and models.

关键词： atmospheric radiation radiative transfer remote sensing radiative forcing climate change

Evaluating the thermal environment of urban land surfaces in Yakutsk,a city located in a region of continuous permafrost

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Advances in Climate Change Research 2024年第1期15卷 113-123页

作者： Xiang-Long LI Ze ZHANG Jin-Xin LU Anatoli BROUCHKOV Qing-Kai YAN Qi-Hao YU Sheng-Rong ZHANG Andrey MELNIKOV School of Civil Engineering and Transportation Institute of Cold Regions Science and EngineeringPermafrost InstituteNortheast Forestry UniversityHarbin150040China China Railway Qinghai-Tibet Group Company Xining 810000China Department of Geocryology Faculty of GeologyLomonosov Moscow State UniversityMoscow 119234Russian Federation State Key Laboratory of Frozen Soil Engineering Northwest Institute of Eco Envi ronment and Re sourcesChinese Academy of SciencesLanzhou 730000China Melnikov Permafrost Institute Siberian BranchRussian Academy of ScienceYakutsk 677010Russian Federation

Rapid urbanization has led to changes in the urban land surface thermal environment.However,there are still much unknown about the urban land surface thermal conditions in permafrost regions.Permafrost is a unique geological environment,changes in the urban land surface thermal environment may trigger geological disasters caused by permafrost degradation.This study utilized remote sensing data and geographic de-tectors to identify the dynamic changes in land surface temperature(LST)and land use/land cover(LU/LC)in Yakutsk,as well as the potential factors contributing to LST variations.Between 1992 and 2020,the built-up area in Yakutsk increased by 36%,and the annual average LST in Yakutsk has risen by 6.67℃,accompanied by an expansion of high-temperature areas.Despite ongoing greening efforts,rapid urbanization poses a threat to these green spaces.Changes in the normalized difference built-up index(NDBI)and land use transfer(LDT)were identified as the primary drivers of urban LST changes.By integrating geographic detector technology and artificial neural network models,we optimized the selection of input factors in the prediction model and used it to explore the future changes in LST in Yakutsk.The average LST in Yakutsk is expected to reach 23.4℃ and 25.1℃ in 2030 and 2040,respectively,with a further increase in high-temperature areas.This study provides a reference for ecological,hydrological,and geological assessments of cities in permafrost regions.

关键词： remote sensing Land surface temperature Land use/Land cover Permafrost Yakutsk

Monitoring Surface Water Change in Northeast China in 1999–2020:Evidence from Satellite Observation and Refined Classification

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Chinese Geographical Science 2024年第1期34卷 106-117页

作者： LIU Kai ZHANG Dapeng CHEN Tan CUI Peipei FAN Chenyu SONG Chunqiao Key Laboratory of Watershed Geographic Sciences Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjing210008China University of Chinese Academy of Sciences Nanjing(UCASNJ)Nanjing211135China Geographic Information and Tourism College Chuzhou UniversityChuzhou239000China

As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.However,the long-term variation characteristics of surface water of different water body types in Northeast China remain rarely explored.This study investigated how surface water bodies of different types(e.g.,lake,reservoir,river,coastal aquaculture,marsh wetland,ephemeral water) changed during1999–2020 in Northeast China based on various remote sensing-based datasets.The results showed that surface water in Northeast China grew dramatically in the past two decades,with an equivalent area increasing from 24 394 km^(2) in 1999 to 34 595 km^(2) in 2020.The surge of ephemeral water is the primary driver of surface water expansion,which could ascribe to shifted precipitation pattern.Marsh wetlands,rivers,and reservoirs experienced a similar trend,with an approximate 20% increase at the interdecadal scale.By contrast,coastal aquacultures and natural lakes remain relatively stable.This study is expected to provide a more comprehensive investigation of the surface water variability in Northeast China and has important practical significance for the scientific management of different types of surface water.

关键词： surface water spatiotemporal variation water body classification remote sensing Northeast China