High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China
Owing to advancements in satellite remote sensing technology, the acquisition of global land surface parameters, notably, the leaf area index (LAI), has become increasingly accessible. The Sentinel-2 (S2) satellite plays an important role in the monitoring of ecological environments and resource man...
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| Format: | Article |
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MDPI AG
2024-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/5/764 |
| _version_ | 1797263962327220224 |
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| author | Changjing Wang Hongmin Zhou Guodong Zhang Jianguo Duan Moxiao Lin |
| author_facet | Changjing Wang Hongmin Zhou Guodong Zhang Jianguo Duan Moxiao Lin |
| author_sort | Changjing Wang |
| collection | DOAJ |
| description | Owing to advancements in satellite remote sensing technology, the acquisition of global land surface parameters, notably, the leaf area index (LAI), has become increasingly accessible. The Sentinel-2 (S2) satellite plays an important role in the monitoring of ecological environments and resource management. The prevalent use of the 20 m spatial resolution band in S2-based inversion models imposes significant limitations on the applicability of S2 data in applications requiring finer spatial resolution. Furthermore, although a substantial body of research on LAI retrieval using S2 data concentrates on agricultural landscapes, studies dedicated to forest ecosystems, although increasing, remain relatively less prevalent. This study aims to establish a viable methodology for retrieving 10 m resolution LAI data in forested regions. The empirical model of the soil adjusted vegetation index (SAVI), the backpack neural network based on simulated annealing (SA-BP) algorithm, and the variational heteroscedastic Gaussian process regression (VHGPR) model are established in this experiment based on the LAI data measured and the corresponding 10 m spatial resolution S2 satellite surface reflectance data in the Saihanba Forestry Center (SFC). The LAI retrieval performance of the three models is then validated using field data, and the error sources of the best performing VHGPR models (R<sup>2</sup> of 0.8696 and RMSE of 0.5078) are further analyzed. Moreover, the VHGPR model stands out for its capacity to quantify the uncertainty in LAI estimation, presenting a notable advantage in assessing the significance of input data, eliminating redundant bands, and being well suited for uncertainty estimation. This feature is particularly valuable in generating accurate LAI products, especially in regions characterized by diverse forest compositions. |
| first_indexed | 2024-04-25T00:21:20Z |
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| id | doaj.art-a21fcc624df247d98f9800d9462d83ad |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-25T00:21:20Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a21fcc624df247d98f9800d9462d83ad2024-03-12T16:53:56ZengMDPI AGRemote Sensing2072-42922024-02-0116576410.3390/rs16050764High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, ChinaChangjing Wang0Hongmin Zhou1Guodong Zhang2Jianguo Duan3Moxiao Lin4State Key Laboratory of Remote Sensing Science, 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, Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaState Key Laboratory of Remote Sensing Science, 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, Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaOwing to advancements in satellite remote sensing technology, the acquisition of global land surface parameters, notably, the leaf area index (LAI), has become increasingly accessible. The Sentinel-2 (S2) satellite plays an important role in the monitoring of ecological environments and resource management. The prevalent use of the 20 m spatial resolution band in S2-based inversion models imposes significant limitations on the applicability of S2 data in applications requiring finer spatial resolution. Furthermore, although a substantial body of research on LAI retrieval using S2 data concentrates on agricultural landscapes, studies dedicated to forest ecosystems, although increasing, remain relatively less prevalent. This study aims to establish a viable methodology for retrieving 10 m resolution LAI data in forested regions. The empirical model of the soil adjusted vegetation index (SAVI), the backpack neural network based on simulated annealing (SA-BP) algorithm, and the variational heteroscedastic Gaussian process regression (VHGPR) model are established in this experiment based on the LAI data measured and the corresponding 10 m spatial resolution S2 satellite surface reflectance data in the Saihanba Forestry Center (SFC). The LAI retrieval performance of the three models is then validated using field data, and the error sources of the best performing VHGPR models (R<sup>2</sup> of 0.8696 and RMSE of 0.5078) are further analyzed. Moreover, the VHGPR model stands out for its capacity to quantify the uncertainty in LAI estimation, presenting a notable advantage in assessing the significance of input data, eliminating redundant bands, and being well suited for uncertainty estimation. This feature is particularly valuable in generating accurate LAI products, especially in regions characterized by diverse forest compositions.https://www.mdpi.com/2072-4292/16/5/764leaf area indexVHGPRSentinel-2retrievalwoodland |
| spellingShingle | Changjing Wang Hongmin Zhou Guodong Zhang Jianguo Duan Moxiao Lin High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China Remote Sensing leaf area index VHGPR Sentinel-2 retrieval woodland |
| title | High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China |
| title_full | High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China |
| title_fullStr | High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China |
| title_full_unstemmed | High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China |
| title_short | High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China |
| title_sort | high spatial resolution leaf area index estimation for woodland in saihanba forestry center china |
| topic | leaf area index VHGPR Sentinel-2 retrieval woodland |
| url | https://www.mdpi.com/2072-4292/16/5/764 |
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