phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data
Satellite-retrieved vegetation phenology has great potential for application in characterizing seasonal and annual land surface dynamics. However, obtaining regional-scale vegetation phenology from satellite remote sensing data often requires extensive data processing and computation, which makes th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2023.1097249/full |
| _version_ | 1811155488754106368 |
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| author | Yongjian Ruan Yongjian Ruan Baozhen Ruan Qinchuan Xin Xi Liao Fengrui Jing Xinchang Zhang |
| author_facet | Yongjian Ruan Yongjian Ruan Baozhen Ruan Qinchuan Xin Xi Liao Fengrui Jing Xinchang Zhang |
| author_sort | Yongjian Ruan |
| collection | DOAJ |
| description | Satellite-retrieved vegetation phenology has great potential for application in characterizing seasonal and annual land surface dynamics. However, obtaining regional-scale vegetation phenology from satellite remote sensing data often requires extensive data processing and computation, which makes the accurate and rapid retrieval of regional-scale phenology a challenge. To retrieve vegetation phenology from satellite remote sensing data, we developed an open-source tool called phenoC++, which uses parallel technology in C++. phenoC++ includes six common algorithms: amplitude threshold (AT), first-order derivative (FOD), second-order derivative (SOD), third-order derivative (TOD), relative change rate (RCR), and curvature change rate (CCR). We implemented the proposed phenoC++ and evaluated its performance on a site scale with PhenoCam-observed phenology metrics. The result shows that SOS derived from MODIS images by phenoC++ with six methods (i.e., AT, FOD, SOD, RCR, TOD, and CCR) obtained r-values of 0.75, 0.76, 0.75, 0.76, 0.64, and 0.67, and RMSE values of 21.36, 20.41, 22.38, 19.11, 33.56, and 32.14, respectively. Satellite-retrieved EOS by phenoC++ with six methods obtained r-values of 0.58, 0.59, 0.57, 0.56, 0.36, and 0.40, and RMSE values of 52.43, 46.68, 55.13, 49.46, 71.13, and 69.34, respectively. Using PhenoCam-observed phenology as a baseline, SOS retrieved by phenoC++ was superior to MCD12Q2, while EOS retrieved by phenoC++ was slightly inferior to that of MCD12Q2. Moreover, compared with MCD12Q2 on a regional scale, phenoC++-retrieved vegetation phenology yields more effective pixels. The innovative features of phenoC++ are 1) integrating six algorithms for retrieving SOS and EOS; 2) quickly processing data on a large scale with simple input startup parameters; 3) outputting phenology metrics in GeoTIFF format image, which is more convenient to use with other geospatial data. phenoC++ could aid in investigating and addressing large-scale phenology problems of the ecological environment. |
| first_indexed | 2024-04-10T04:33:51Z |
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| id | doaj.art-62b21488f0cc4d55b64dba5ba62647d2 |
| institution | Directory Open Access Journal |
| issn | 2296-665X |
| language | English |
| last_indexed | 2024-04-10T04:33:51Z |
| publishDate | 2023-03-01 |
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| series | Frontiers in Environmental Science |
| spelling | doaj.art-62b21488f0cc4d55b64dba5ba62647d22023-03-10T04:52:15ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2023-03-011110.3389/fenvs.2023.10972491097249phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing dataYongjian Ruan0Yongjian Ruan1Baozhen Ruan2Qinchuan Xin3Xi Liao4Fengrui Jing5Xinchang Zhang6School of Geography and Remote Sensing, Guangzhou University, Guangzhou, ChinaKey Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou, ChinaSchool of Geography and Planning, Sun Yat-Sen University, Guangzhou, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou, ChinaDepartment of Geography, University of South Carolina, Columbia, SC, United StatesSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou, ChinaSatellite-retrieved vegetation phenology has great potential for application in characterizing seasonal and annual land surface dynamics. However, obtaining regional-scale vegetation phenology from satellite remote sensing data often requires extensive data processing and computation, which makes the accurate and rapid retrieval of regional-scale phenology a challenge. To retrieve vegetation phenology from satellite remote sensing data, we developed an open-source tool called phenoC++, which uses parallel technology in C++. phenoC++ includes six common algorithms: amplitude threshold (AT), first-order derivative (FOD), second-order derivative (SOD), third-order derivative (TOD), relative change rate (RCR), and curvature change rate (CCR). We implemented the proposed phenoC++ and evaluated its performance on a site scale with PhenoCam-observed phenology metrics. The result shows that SOS derived from MODIS images by phenoC++ with six methods (i.e., AT, FOD, SOD, RCR, TOD, and CCR) obtained r-values of 0.75, 0.76, 0.75, 0.76, 0.64, and 0.67, and RMSE values of 21.36, 20.41, 22.38, 19.11, 33.56, and 32.14, respectively. Satellite-retrieved EOS by phenoC++ with six methods obtained r-values of 0.58, 0.59, 0.57, 0.56, 0.36, and 0.40, and RMSE values of 52.43, 46.68, 55.13, 49.46, 71.13, and 69.34, respectively. Using PhenoCam-observed phenology as a baseline, SOS retrieved by phenoC++ was superior to MCD12Q2, while EOS retrieved by phenoC++ was slightly inferior to that of MCD12Q2. Moreover, compared with MCD12Q2 on a regional scale, phenoC++-retrieved vegetation phenology yields more effective pixels. The innovative features of phenoC++ are 1) integrating six algorithms for retrieving SOS and EOS; 2) quickly processing data on a large scale with simple input startup parameters; 3) outputting phenology metrics in GeoTIFF format image, which is more convenient to use with other geospatial data. phenoC++ could aid in investigating and addressing large-scale phenology problems of the ecological environment.https://www.frontiersin.org/articles/10.3389/fenvs.2023.1097249/fullvegetation phenologysatellite remote sensing dataPhenoCamC++ languagethe contiguous United States |
| spellingShingle | Yongjian Ruan Yongjian Ruan Baozhen Ruan Qinchuan Xin Xi Liao Fengrui Jing Xinchang Zhang phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data Frontiers in Environmental Science vegetation phenology satellite remote sensing data PhenoCam C++ language the contiguous United States |
| title | phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data |
| title_full | phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data |
| title_fullStr | phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data |
| title_full_unstemmed | phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data |
| title_short | phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data |
| title_sort | phenoc an open source tool for retrieving vegetation phenology from satellite remote sensing data |
| topic | vegetation phenology satellite remote sensing data PhenoCam C++ language the contiguous United States |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2023.1097249/full |
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