Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020
Understanding the net ecosystem productivity (NEP) is essential for understanding ecosystem functioning and the global carbon cycle. Utilizing meteorological and The Advanced Very High Resolution Radiometer (AVHRR) remote sensing data, this study employed the Carnegie–Ames–Stanford Approach (CASA) a...
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MDPI AG
2023-12-01
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| Online Access: | https://www.mdpi.com/2072-4292/16/1/60 |
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| author | Yang Chen Yongming Xu Tianyu Chen Fei Zhang Shanyou Zhu |
| author_facet | Yang Chen Yongming Xu Tianyu Chen Fei Zhang Shanyou Zhu |
| author_sort | Yang Chen |
| collection | DOAJ |
| description | Understanding the net ecosystem productivity (NEP) is essential for understanding ecosystem functioning and the global carbon cycle. Utilizing meteorological and The Advanced Very High Resolution Radiometer (AVHRR) remote sensing data, this study employed the Carnegie–Ames–Stanford Approach (CASA) and the Geostatistical Model of Soil Respiration (GSMSR) to map a monthly vegetation NEP in China from 1982 to 2020. Then, we examined the spatiotemporal trends of NEP and identified the drivers of NEP changes using the Geodetector model. The mean NEP over the 39-year period amounted to 265.38 gC·m<sup>−2</sup>. Additionally, the average annual carbon sequestration amounted to 1.89 PgC, indicating a large carbon sink effect. From 1982 to 2020, there was a general fluctuating increasing trend observed in the annual mean NEP, exhibiting an overall average growth rate of 4.69 gC·m<sup>−2</sup>·a<sup>−1</sup>. The analysis revealed that the majority of the vegetation region in China, accounting for 93.45% of the entirety, exhibited increasing trends in NEP. According to the Geodetector analysis, precipitation change rate, solar radiation change rate, and altitude were the key driving factors in NEP change rate. Furthermore, the interaction between the precipitation change rate and altitude demonstrated the most significant effect. |
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| format | Article |
| id | doaj.art-f8956750e6474d6b84c1a40d9f597680 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T14:58:55Z |
| publishDate | 2023-12-01 |
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| spelling | doaj.art-f8956750e6474d6b84c1a40d9f5976802024-01-10T15:07:15ZengMDPI AGRemote Sensing2072-42922023-12-011616010.3390/rs16010060Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020Yang Chen0Yongming Xu1Tianyu Chen2Fei Zhang3Shanyou Zhu4School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaUnderstanding the net ecosystem productivity (NEP) is essential for understanding ecosystem functioning and the global carbon cycle. Utilizing meteorological and The Advanced Very High Resolution Radiometer (AVHRR) remote sensing data, this study employed the Carnegie–Ames–Stanford Approach (CASA) and the Geostatistical Model of Soil Respiration (GSMSR) to map a monthly vegetation NEP in China from 1982 to 2020. Then, we examined the spatiotemporal trends of NEP and identified the drivers of NEP changes using the Geodetector model. The mean NEP over the 39-year period amounted to 265.38 gC·m<sup>−2</sup>. Additionally, the average annual carbon sequestration amounted to 1.89 PgC, indicating a large carbon sink effect. From 1982 to 2020, there was a general fluctuating increasing trend observed in the annual mean NEP, exhibiting an overall average growth rate of 4.69 gC·m<sup>−2</sup>·a<sup>−1</sup>. The analysis revealed that the majority of the vegetation region in China, accounting for 93.45% of the entirety, exhibited increasing trends in NEP. According to the Geodetector analysis, precipitation change rate, solar radiation change rate, and altitude were the key driving factors in NEP change rate. Furthermore, the interaction between the precipitation change rate and altitude demonstrated the most significant effect.https://www.mdpi.com/2072-4292/16/1/60NEPspatiotemporal variationsAVHRRCASAGeodetector |
| spellingShingle | Yang Chen Yongming Xu Tianyu Chen Fei Zhang Shanyou Zhu Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020 Remote Sensing NEP spatiotemporal variations AVHRR CASA Geodetector |
| title | Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020 |
| title_full | Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020 |
| title_fullStr | Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020 |
| title_full_unstemmed | Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020 |
| title_short | Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020 |
| title_sort | exploring the spatiotemporal dynamics and driving factors of net ecosystem productivity in china from 1982 to 2020 |
| topic | NEP spatiotemporal variations AVHRR CASA Geodetector |
| url | https://www.mdpi.com/2072-4292/16/1/60 |
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