Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods
Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution....
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/9/2460 |
| _version_ | 1797569585794252800 |
|---|---|
| author | Yangyang Zhang Jian Yang Xiuguo Liu Lin Du Shuo Shi Jia Sun Biwu Chen |
| author_facet | Yangyang Zhang Jian Yang Xiuguo Liu Lin Du Shuo Shi Jia Sun Biwu Chen |
| author_sort | Yangyang Zhang |
| collection | DOAJ |
| description | Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m<sup>2</sup>/m<sup>2</sup>) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation. |
| first_indexed | 2024-03-10T20:13:15Z |
| format | Article |
| id | doaj.art-a42970d6c30b4f01bea726eb124a5ad5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:13:15Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a42970d6c30b4f01bea726eb124a5ad52023-11-19T22:46:06ZengMDPI AGSensors1424-82202020-04-01209246010.3390/s20092460Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression MethodsYangyang Zhang0Jian Yang1Xiuguo Liu2Lin Du3Shuo Shi4Jia Sun5Biwu Chen6School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaLeaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m<sup>2</sup>/m<sup>2</sup>) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.https://www.mdpi.com/1424-8220/20/9/2460leaf area index (LAI)look-up table (LUT)Gaussian process regression (GPR)GF-1PROSAIL |
| spellingShingle | Yangyang Zhang Jian Yang Xiuguo Liu Lin Du Shuo Shi Jia Sun Biwu Chen Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods Sensors leaf area index (LAI) look-up table (LUT) Gaussian process regression (GPR) GF-1 PROSAIL |
| title | Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods |
| title_full | Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods |
| title_fullStr | Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods |
| title_full_unstemmed | Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods |
| title_short | Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods |
| title_sort | estimation of multi species leaf area index based on chinese gf 1 satellite data using look up table and gaussian process regression methods |
| topic | leaf area index (LAI) look-up table (LUT) Gaussian process regression (GPR) GF-1 PROSAIL |
| url | https://www.mdpi.com/1424-8220/20/9/2460 |
| work_keys_str_mv | AT yangyangzhang estimationofmultispeciesleafareaindexbasedonchinesegf1satellitedatausinglookuptableandgaussianprocessregressionmethods AT jianyang estimationofmultispeciesleafareaindexbasedonchinesegf1satellitedatausinglookuptableandgaussianprocessregressionmethods AT xiuguoliu estimationofmultispeciesleafareaindexbasedonchinesegf1satellitedatausinglookuptableandgaussianprocessregressionmethods AT lindu estimationofmultispeciesleafareaindexbasedonchinesegf1satellitedatausinglookuptableandgaussianprocessregressionmethods AT shuoshi estimationofmultispeciesleafareaindexbasedonchinesegf1satellitedatausinglookuptableandgaussianprocessregressionmethods AT jiasun estimationofmultispeciesleafareaindexbasedonchinesegf1satellitedatausinglookuptableandgaussianprocessregressionmethods AT biwuchen estimationofmultispeciesleafareaindexbasedonchinesegf1satellitedatausinglookuptableandgaussianprocessregressionmethods |