Retrieval of the Leaf Area Index from MODIS Top-of-Atmosphere Reflectance Data Using a Neural Network Supported by Simulation Data
The leaf area index (LAI), a key parameter used to characterize the structure and function of the vegetation canopy, is crucial to simulations of the carbon, nitrogen, and water cycles of Earth’s system. In this paper, a neural network (NN) method coupled with vegetation canopy and atmospheric radia...
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/10/2456 |
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| author | Weiyan Wang Yingying Ma Xiaoliang Meng Lin Sun Chen Jia Shikuan Jin Hui Li |
| author_facet | Weiyan Wang Yingying Ma Xiaoliang Meng Lin Sun Chen Jia Shikuan Jin Hui Li |
| author_sort | Weiyan Wang |
| collection | DOAJ |
| description | The leaf area index (LAI), a key parameter used to characterize the structure and function of the vegetation canopy, is crucial to simulations of the carbon, nitrogen, and water cycles of Earth’s system. In this paper, a neural network (NN) method coupled with vegetation canopy and atmospheric radiative transfer (RT) simulations is proposed to realize LAI retrieval without prior data support and complex atmospheric corrections. The look-up table (LUT) of the top-of-atmosphere (TOA) reflectance and associated input variables was simulated by 6S (6S simulation) based on the top-of-canopy (TOC) reflectance LUT simulated by PROSAIL. This was then used to train the NN to obtain the LAI inversion model. This method has been successfully applied to MODIS L1B data (MOD021KM), and the LAI retrieval of the vegetation canopy was realized. The estimated LAI was compared with the MODIS LAI (MOD15A2H) using mid-latitude summer data from 2000 to 2017 in the DIRECT 2.0 ground database. The experiments indicated that the LAI retrieved by the TOA reflectance (<i>r</i> = 0.7852, RMSE = 0.5191) was not much different from the LAI retrieved by the TOC reflectance (<i>r</i> = 0.8063, RMSE = 0.7669), and the accuracy was better than the MODIS LAI (<i>r</i> = 0.7607, RMSE = 0.8239), which proves the feasibility of this method. |
| first_indexed | 2024-03-10T01:55:54Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T01:55:54Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-58115e4cc25b4afe97a2a1e55dc3ea0a2023-11-23T12:56:36ZengMDPI AGRemote Sensing2072-42922022-05-011410245610.3390/rs14102456Retrieval of the Leaf Area Index from MODIS Top-of-Atmosphere Reflectance Data Using a Neural Network Supported by Simulation DataWeiyan Wang0Yingying Ma1Xiaoliang Meng2Lin Sun3Chen Jia4Shikuan Jin5Hui Li6State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaThe leaf area index (LAI), a key parameter used to characterize the structure and function of the vegetation canopy, is crucial to simulations of the carbon, nitrogen, and water cycles of Earth’s system. In this paper, a neural network (NN) method coupled with vegetation canopy and atmospheric radiative transfer (RT) simulations is proposed to realize LAI retrieval without prior data support and complex atmospheric corrections. The look-up table (LUT) of the top-of-atmosphere (TOA) reflectance and associated input variables was simulated by 6S (6S simulation) based on the top-of-canopy (TOC) reflectance LUT simulated by PROSAIL. This was then used to train the NN to obtain the LAI inversion model. This method has been successfully applied to MODIS L1B data (MOD021KM), and the LAI retrieval of the vegetation canopy was realized. The estimated LAI was compared with the MODIS LAI (MOD15A2H) using mid-latitude summer data from 2000 to 2017 in the DIRECT 2.0 ground database. The experiments indicated that the LAI retrieved by the TOA reflectance (<i>r</i> = 0.7852, RMSE = 0.5191) was not much different from the LAI retrieved by the TOC reflectance (<i>r</i> = 0.8063, RMSE = 0.7669), and the accuracy was better than the MODIS LAI (<i>r</i> = 0.7607, RMSE = 0.8239), which proves the feasibility of this method.https://www.mdpi.com/2072-4292/14/10/2456leaf area index (LAI)MODISneural network (NN)PROSAILsecond simulation of satellite signal in the solar spectrum (6S)inversion |
| spellingShingle | Weiyan Wang Yingying Ma Xiaoliang Meng Lin Sun Chen Jia Shikuan Jin Hui Li Retrieval of the Leaf Area Index from MODIS Top-of-Atmosphere Reflectance Data Using a Neural Network Supported by Simulation Data Remote Sensing leaf area index (LAI) MODIS neural network (NN) PROSAIL second simulation of satellite signal in the solar spectrum (6S) inversion |
| title | Retrieval of the Leaf Area Index from MODIS Top-of-Atmosphere Reflectance Data Using a Neural Network Supported by Simulation Data |
| title_full | Retrieval of the Leaf Area Index from MODIS Top-of-Atmosphere Reflectance Data Using a Neural Network Supported by Simulation Data |
| title_fullStr | Retrieval of the Leaf Area Index from MODIS Top-of-Atmosphere Reflectance Data Using a Neural Network Supported by Simulation Data |
| title_full_unstemmed | Retrieval of the Leaf Area Index from MODIS Top-of-Atmosphere Reflectance Data Using a Neural Network Supported by Simulation Data |
| title_short | Retrieval of the Leaf Area Index from MODIS Top-of-Atmosphere Reflectance Data Using a Neural Network Supported by Simulation Data |
| title_sort | retrieval of the leaf area index from modis top of atmosphere reflectance data using a neural network supported by simulation data |
| topic | leaf area index (LAI) MODIS neural network (NN) PROSAIL second simulation of satellite signal in the solar spectrum (6S) inversion |
| url | https://www.mdpi.com/2072-4292/14/10/2456 |
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