Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China
Net ecosystem productivity (NEP) plays an important role in understanding ecosystem function and the global carbon cycle. In this paper, the key parameters of the Carnegie Ames Stanford Approach (CASA) model, maximum light use efficiency (ε<sub>max</sub>), was optimized by using vegetati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/8/1902 |
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| author | Liang Liang Di Geng Juan Yan Siyi Qiu Yanyan Shi Shuguo Wang Lijuan Wang Lianpeng Zhang Jianrong Kang |
| author_facet | Liang Liang Di Geng Juan Yan Siyi Qiu Yanyan Shi Shuguo Wang Lijuan Wang Lianpeng Zhang Jianrong Kang |
| author_sort | Liang Liang |
| collection | DOAJ |
| description | Net ecosystem productivity (NEP) plays an important role in understanding ecosystem function and the global carbon cycle. In this paper, the key parameters of the Carnegie Ames Stanford Approach (CASA) model, maximum light use efficiency (ε<sub>max</sub>), was optimized by using vegetation classification data. Then, the NEP was estimated by coupling the optimized CASA model, geostatistical model of soil respiration (GSMSR) and the soil respiration–soil heterotrophic respiration (R<sub>s</sub>-R<sub>h</sub>) relationship model. The ground observations from ChinaFLUX were used to verify the NEP estimation accuracy. The results showed that the R<sup>2</sup> of the optimized CASA model increased from 0.411 to 0.774, and RMSE decreased from 21.425 gC·m<sup>−2</sup>·month<sup>−1</sup> to 12.045 gC·m<sup>−2</sup>·month<sup>−1</sup>, indicating that optimizing CASA model by vegetation classification data was an effective method to improve the estimation accuracy of NEP. On this basis, the spatial and temporal distribution of NEP in China was analyzed. The research indicated that the monthly variation of NEP in China was a single peak curve with summer as the peak, which generally presented the pattern of southern region > northern region > Qinghai–Tibet region > northwest region. Furthermore, from 2001 to 2016, most regions of China showed a non-significant level upward trend, but main cropland (e.g., North China Plain and Northeast Plain) and some grassland (e.g., Ngari in Qinghai–Tibet Plateau and Xilin Gol League in Inner Mongolia) showed a non-significant-level downward trend. The study can deepen the understanding of the distribution of carbon sources/sinks in China, and provide a reference for regional carbon cycle research. |
| first_indexed | 2024-03-09T13:03:53Z |
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| id | doaj.art-80914070e7e8436a9c1cc95bb8abd6b5 |
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| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T13:03:53Z |
| publishDate | 2022-04-01 |
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| series | Remote Sensing |
| spelling | doaj.art-80914070e7e8436a9c1cc95bb8abd6b52023-11-30T21:51:23ZengMDPI AGRemote Sensing2072-42922022-04-01148190210.3390/rs14081902Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in ChinaLiang Liang0Di Geng1Juan Yan2Siyi Qiu3Yanyan Shi4Shuguo Wang5Lijuan Wang6Lianpeng Zhang7Jianrong Kang8School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, ChinaNet ecosystem productivity (NEP) plays an important role in understanding ecosystem function and the global carbon cycle. In this paper, the key parameters of the Carnegie Ames Stanford Approach (CASA) model, maximum light use efficiency (ε<sub>max</sub>), was optimized by using vegetation classification data. Then, the NEP was estimated by coupling the optimized CASA model, geostatistical model of soil respiration (GSMSR) and the soil respiration–soil heterotrophic respiration (R<sub>s</sub>-R<sub>h</sub>) relationship model. The ground observations from ChinaFLUX were used to verify the NEP estimation accuracy. The results showed that the R<sup>2</sup> of the optimized CASA model increased from 0.411 to 0.774, and RMSE decreased from 21.425 gC·m<sup>−2</sup>·month<sup>−1</sup> to 12.045 gC·m<sup>−2</sup>·month<sup>−1</sup>, indicating that optimizing CASA model by vegetation classification data was an effective method to improve the estimation accuracy of NEP. On this basis, the spatial and temporal distribution of NEP in China was analyzed. The research indicated that the monthly variation of NEP in China was a single peak curve with summer as the peak, which generally presented the pattern of southern region > northern region > Qinghai–Tibet region > northwest region. Furthermore, from 2001 to 2016, most regions of China showed a non-significant level upward trend, but main cropland (e.g., North China Plain and Northeast Plain) and some grassland (e.g., Ngari in Qinghai–Tibet Plateau and Xilin Gol League in Inner Mongolia) showed a non-significant-level downward trend. The study can deepen the understanding of the distribution of carbon sources/sinks in China, and provide a reference for regional carbon cycle research.https://www.mdpi.com/2072-4292/14/8/1902NEPCASA modelε<sub>max</sub>carbon sinkspatiotemporal pattern |
| spellingShingle | Liang Liang Di Geng Juan Yan Siyi Qiu Yanyan Shi Shuguo Wang Lijuan Wang Lianpeng Zhang Jianrong Kang Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China Remote Sensing NEP CASA model ε<sub>max</sub> carbon sink spatiotemporal pattern |
| title | Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China |
| title_full | Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China |
| title_fullStr | Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China |
| title_full_unstemmed | Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China |
| title_short | Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China |
| title_sort | remote sensing estimation and spatiotemporal pattern analysis of terrestrial net ecosystem productivity in china |
| topic | NEP CASA model ε<sub>max</sub> carbon sink spatiotemporal pattern |
| url | https://www.mdpi.com/2072-4292/14/8/1902 |
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