Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product
Despite advances in remote sensing–based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPP<sub>MOD</sub>) is less well understood over rice–wheat-rotation cropland. To improve the performance of GPP<...
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MDPI AG
2021-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/21/4229 |
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| author | Zexia Duan Yuanjian Yang Shaohui Zhou Zhiqiu Gao Lian Zong Sihui Fan Jian Yin |
| author_facet | Zexia Duan Yuanjian Yang Shaohui Zhou Zhiqiu Gao Lian Zong Sihui Fan Jian Yin |
| author_sort | Zexia Duan |
| collection | DOAJ |
| description | Despite advances in remote sensing–based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPP<sub>MOD</sub>) is less well understood over rice–wheat-rotation cropland. To improve the performance of GPP<sub>MOD</sub>, a random forest (RF) machine learning model was constructed and employed over the rice–wheat double-cropping fields of eastern China. The RF-derived GPP (GPP<sub>RF</sub>) agreed well with the eddy covariance (EC)-derived GPP (GPP<sub>EC</sub>), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m<sup>−2</sup> d<sup>−1</sup>. Therefore, it was deemed reliable to upscale GPP<sub>EC</sub> to regional scales through the RF model. The upscaled cumulative seasonal GPP<sub>RF</sub> was higher for rice (924 g C m<sup>−2</sup>) than that for wheat (532 g C m<sup>−2</sup>). By comparing GPP<sub>MOD</sub> and GPP<sub>EC</sub>, we found that GPP<sub>MOD</sub> performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPP<sub>MOD</sub> was calibrated by GPP<sub>RF</sub>, and the error range of GPP<sub>MOD</sub> (GPP<sub>RF</sub> minus GPP<sub>MOD</sub>) was found to be 2.5–3.25 g C m<sup>−2</sup> d<sup>−1</sup> for rice and 0.75–1.25 g C m<sup>−2</sup> d<sup>−1</sup> for wheat. Our findings suggest that RF-based GPP products have the potential to be applied in accurately evaluating MODIS-based agroecosystem carbon cycles at regional or even global scales. |
| first_indexed | 2024-03-10T05:53:40Z |
| format | Article |
| id | doaj.art-32470ff64b074589acd4d81a34a23e3e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T05:53:40Z |
| publishDate | 2021-10-01 |
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| series | Remote Sensing |
| spelling | doaj.art-32470ff64b074589acd4d81a34a23e3e2023-11-22T21:30:22ZengMDPI AGRemote Sensing2072-42922021-10-011321422910.3390/rs13214229Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS ProductZexia Duan0Yuanjian Yang1Shaohui Zhou2Zhiqiu Gao3Lian Zong4Sihui Fan5Jian Yin6Climate and Weather Disasters Collaborative Innovation Center, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaClimate and Weather Disasters Collaborative Innovation Center, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaClimate and Weather Disasters Collaborative Innovation Center, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaClimate and Weather Disasters Collaborative Innovation Center, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaClimate and Weather Disasters Collaborative Innovation Center, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaNingbo Meteorological Bureau, Ningbo 315000, ChinaClimate and Weather Disasters Collaborative Innovation Center, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaDespite advances in remote sensing–based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPP<sub>MOD</sub>) is less well understood over rice–wheat-rotation cropland. To improve the performance of GPP<sub>MOD</sub>, a random forest (RF) machine learning model was constructed and employed over the rice–wheat double-cropping fields of eastern China. The RF-derived GPP (GPP<sub>RF</sub>) agreed well with the eddy covariance (EC)-derived GPP (GPP<sub>EC</sub>), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m<sup>−2</sup> d<sup>−1</sup>. Therefore, it was deemed reliable to upscale GPP<sub>EC</sub> to regional scales through the RF model. The upscaled cumulative seasonal GPP<sub>RF</sub> was higher for rice (924 g C m<sup>−2</sup>) than that for wheat (532 g C m<sup>−2</sup>). By comparing GPP<sub>MOD</sub> and GPP<sub>EC</sub>, we found that GPP<sub>MOD</sub> performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPP<sub>MOD</sub> was calibrated by GPP<sub>RF</sub>, and the error range of GPP<sub>MOD</sub> (GPP<sub>RF</sub> minus GPP<sub>MOD</sub>) was found to be 2.5–3.25 g C m<sup>−2</sup> d<sup>−1</sup> for rice and 0.75–1.25 g C m<sup>−2</sup> d<sup>−1</sup> for wheat. Our findings suggest that RF-based GPP products have the potential to be applied in accurately evaluating MODIS-based agroecosystem carbon cycles at regional or even global scales.https://www.mdpi.com/2072-4292/13/21/4229random forestgross primary productivityeddy covarianceMOD17A2Hrice–wheat rotation cropland |
| spellingShingle | Zexia Duan Yuanjian Yang Shaohui Zhou Zhiqiu Gao Lian Zong Sihui Fan Jian Yin Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product Remote Sensing random forest gross primary productivity eddy covariance MOD17A2H rice–wheat rotation cropland |
| title | Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product |
| title_full | Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product |
| title_fullStr | Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product |
| title_full_unstemmed | Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product |
| title_short | Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product |
| title_sort | estimating gross primary productivity gpp over rice wheat rotation croplands by using the random forest model and eddy covariance measurements upscaling and comparison with the modis product |
| topic | random forest gross primary productivity eddy covariance MOD17A2H rice–wheat rotation cropland |
| url | https://www.mdpi.com/2072-4292/13/21/4229 |
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