Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges
For many applications, raw satellite observations need to be converted to high-level products of various essential environmental variables. While numerous products are available at kilometer spatial resolutions, there are few global products at high spatial resolutions (10–30 m), which are also refe...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017224000361 |
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| author | Shunlin Liang Tao He Jianxi Huang Aolin Jia Yuzhen Zhang Yunfeng Cao Xiaona Chen Xidong Chen Jie Cheng Bo Jiang Huaan Jin Ainong Li Siwei Li Xuecao Li Liangyun Liu Xiaobang Liu Han Ma Yichuan Ma Dan-Xia Song Lin Sun Yunjun Yao Wenping Yuan Guodong Zhang Yufang Zhang Liulin Song |
| author_facet | Shunlin Liang Tao He Jianxi Huang Aolin Jia Yuzhen Zhang Yunfeng Cao Xiaona Chen Xidong Chen Jie Cheng Bo Jiang Huaan Jin Ainong Li Siwei Li Xuecao Li Liangyun Liu Xiaobang Liu Han Ma Yichuan Ma Dan-Xia Song Lin Sun Yunjun Yao Wenping Yuan Guodong Zhang Yufang Zhang Liulin Song |
| author_sort | Shunlin Liang |
| collection | DOAJ |
| description | For many applications, raw satellite observations need to be converted to high-level products of various essential environmental variables. While numerous products are available at kilometer spatial resolutions, there are few global products at high spatial resolutions (10–30 m), which are also referred to fine or medium resolutions in the literature. To facilitate the development of more high spatial resolution products, this paper systematically reviews the state-of-the-art progress on inversion algorithms and publicly available regional and global products. We begin with an inventory of available high-resolution satellite data, and then present different algorithms for determining cloud masks, estimating aerosol optical depth, and performing atmospheric correction and topographic correction for land surface reflectance retrieval. The majority of this paper reviews the inversion algorithms and existing regional to global products of 18 variables in four major categories: 1) Land surface radiation, including broadband albedo, land surface temperature, and all-wave net radiation; 2) Terrestrial ecosystem variables, including leaf area index, fraction of absorbed photosynthetically active radiation, fractional vegetation cover, fractional forest cover, tree height, forest above-ground biomass gross primary production, net primary production, and agricultural crop yield; 3) Water cycle and cryosphere, including soil moisture, evapotranspiration, and snow cover; and 4) Land surface types, such as global land cover, impervious surface, inland water, crop type, and fire. Since the existing products over large regions are usually spatially discontinuous due to cloud contamination, different data fusion and data assimilation algorithms and some products for producing spatially seamless and temporally continuous products are presented. In the end, we discuss a variety of challenges in generating global high spatial resolution satellite products. |
| format | Article |
| id | doaj-art-6eb4e88a9de547e8aed9553e1cc5f20a |
| institution | OA Journals |
| issn | 2666-0172 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj-art-6eb4e88a9de547e8aed9553e1cc5f20a2025-08-20T02:34:20ZengElsevierScience of Remote Sensing2666-01722024-12-011010015210.1016/j.srs.2024.100152Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challengesShunlin Liang0Tao He1Jianxi Huang2Aolin Jia3Yuzhen Zhang4Yunfeng Cao5Xiaona Chen6Xidong Chen7Jie Cheng8Bo Jiang9Huaan Jin10Ainong Li11Siwei Li12Xuecao Li13Liangyun Liu14Xiaobang Liu15Han Ma16Yichuan Ma17Dan-Xia Song18Lin Sun19Yunjun Yao20Wenping Yuan21Guodong Zhang22Yufang Zhang23Liulin Song24Jockey Club STEM Laboratory of Quantitative Remote Sensing, Department of Geography, University of Hong Kong, 999077, Hong Kong, China; Corresponding author.Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing, 100083, China; Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, 100083, ChinaEnvironmental Research and Innovation (ERIN) Department, Luxembourg Institute of Science and Technology (LIST), 41 Rue du Brill, L-4422, Belvaux, LuxembourgBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, ChinaPrecision Forestry Key Laboratory of Beijing, Beijing Forestry University, 100083, Beijing, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, ChinaFuture Urbanity & Sustainable Environment (FUSE) Lab, University of Hong Kong, 999007, Hong Kong, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, ChinaResearch Center of Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, 610041, China; Wanglang Mountain Remote Sensing Observation and Research Station of Sichuan Province, Mianyang, 621000, ChinaResearch Center of Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, 610041, China; Wanglang Mountain Remote Sensing Observation and Research Station of Sichuan Province, Mianyang, 621000, ChinaHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing, 100083, China; Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, 100083, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, ChinaThe 27th Research Institute of China Electronics Technology Group Corporation, Zhengzhou, 450047, ChinaJockey Club STEM Laboratory of Quantitative Remote Sensing, Department of Geography, University of Hong Kong, 999077, Hong Kong, ChinaHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaHubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan, 430079, China; College of Urban and Environmental Sciences, Central China Normal University, Wuhan, 430079, ChinaGeomatics College, Shandong University of Science and Technology, Qingdao, Shandong, 266590, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, ChinaInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100091, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, ChinaSchool of Software, Northwestern Polytechnical University, Xi'an, 710072, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, ChinaFor many applications, raw satellite observations need to be converted to high-level products of various essential environmental variables. While numerous products are available at kilometer spatial resolutions, there are few global products at high spatial resolutions (10–30 m), which are also referred to fine or medium resolutions in the literature. To facilitate the development of more high spatial resolution products, this paper systematically reviews the state-of-the-art progress on inversion algorithms and publicly available regional and global products. We begin with an inventory of available high-resolution satellite data, and then present different algorithms for determining cloud masks, estimating aerosol optical depth, and performing atmospheric correction and topographic correction for land surface reflectance retrieval. The majority of this paper reviews the inversion algorithms and existing regional to global products of 18 variables in four major categories: 1) Land surface radiation, including broadband albedo, land surface temperature, and all-wave net radiation; 2) Terrestrial ecosystem variables, including leaf area index, fraction of absorbed photosynthetically active radiation, fractional vegetation cover, fractional forest cover, tree height, forest above-ground biomass gross primary production, net primary production, and agricultural crop yield; 3) Water cycle and cryosphere, including soil moisture, evapotranspiration, and snow cover; and 4) Land surface types, such as global land cover, impervious surface, inland water, crop type, and fire. Since the existing products over large regions are usually spatially discontinuous due to cloud contamination, different data fusion and data assimilation algorithms and some products for producing spatially seamless and temporally continuous products are presented. In the end, we discuss a variety of challenges in generating global high spatial resolution satellite products.http://www.sciencedirect.com/science/article/pii/S2666017224000361Satellite productsHigh-resolutionLandAlgorithmLandsat |
| spellingShingle | Shunlin Liang Tao He Jianxi Huang Aolin Jia Yuzhen Zhang Yunfeng Cao Xiaona Chen Xidong Chen Jie Cheng Bo Jiang Huaan Jin Ainong Li Siwei Li Xuecao Li Liangyun Liu Xiaobang Liu Han Ma Yichuan Ma Dan-Xia Song Lin Sun Yunjun Yao Wenping Yuan Guodong Zhang Yufang Zhang Liulin Song Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges Science of Remote Sensing Satellite products High-resolution Land Algorithm Landsat |
| title | Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges |
| title_full | Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges |
| title_fullStr | Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges |
| title_full_unstemmed | Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges |
| title_short | Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges |
| title_sort | advancements in high resolution land surface satellite products a comprehensive review of inversion algorithms products and challenges |
| topic | Satellite products High-resolution Land Algorithm Landsat |
| url | http://www.sciencedirect.com/science/article/pii/S2666017224000361 |
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