Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion
Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/3/244 |
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| author | Gaofei Yin Aleixandre Verger Yonghua Qu Wei Zhao Baodong Xu Yelu Zeng Ke Liu Jing Li Qinhuo Liu |
| author_facet | Gaofei Yin Aleixandre Verger Yonghua Qu Wei Zhao Baodong Xu Yelu Zeng Ke Liu Jing Li Qinhuo Liu |
| author_sort | Gaofei Yin |
| collection | DOAJ |
| description | Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R<sup>2</sup> = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products. |
| first_indexed | 2024-12-10T16:48:35Z |
| format | Article |
| id | doaj.art-b820673bb0284a29b61ba6d06313f1b5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-10T16:48:35Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b820673bb0284a29b61ba6d06313f1b52022-12-22T01:40:59ZengMDPI AGRemote Sensing2072-42922019-01-0111324410.3390/rs11030244rs11030244Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data FusionGaofei Yin0Aleixandre Verger1Yonghua Qu2Wei Zhao3Baodong Xu4Yelu Zeng5Ke Liu6Jing Li7Qinhuo Liu8Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaCREAF, 08193 Cerdanyola del Vallès, Catalonia, SpainState Key Laboratory of Remote Sensing Science, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Institute of Remote Sensing Science and Engineering, Faculty of Geography Science, Beijing Normal University, Beijing 100875, ChinaResearch Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy Sciences, Chengdu 610010, ChinaMacro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, Wuhan 430070, ChinaDepartment of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USAInstitute of Remote Sensing Application, Sichuan Academy of Agricultural Science, Chengdu 610066, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaMany applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R<sup>2</sup> = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products.https://www.mdpi.com/2072-4292/11/3/244leaf area indexuncertaintyGaussian processeswireless sensor networkdata fusionLandsatMODISvalidation |
| spellingShingle | Gaofei Yin Aleixandre Verger Yonghua Qu Wei Zhao Baodong Xu Yelu Zeng Ke Liu Jing Li Qinhuo Liu Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion Remote Sensing leaf area index uncertainty Gaussian processes wireless sensor network data fusion Landsat MODIS validation |
| title | Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion |
| title_full | Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion |
| title_fullStr | Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion |
| title_full_unstemmed | Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion |
| title_short | Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion |
| title_sort | retrieval of high spatiotemporal resolution leaf area index with gaussian processes wireless sensor network and satellite data fusion |
| topic | leaf area index uncertainty Gaussian processes wireless sensor network data fusion Landsat MODIS validation |
| url | https://www.mdpi.com/2072-4292/11/3/244 |
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