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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2021
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oai:doaj.org-article:32470ff64b074589acd4d81a34a23e3e2021-11-11T18:50:40ZEstimating 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 Product10.3390/rs132142292072-4292https://doaj.org/article/32470ff64b074589acd4d81a34a23e3e2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4229https://doaj.org/toc/2072-4292Despite 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.Zexia DuanYuanjian YangShaohui ZhouZhiqiu GaoLian ZongSihui FanJian YinMDPI AGarticlerandom forestgross primary productivityeddy covarianceMOD17A2Hrice–wheat rotation croplandScienceQENRemote Sensing, Vol 13, Iss 4229, p 4229 (2021) |
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random forest gross primary productivity eddy covariance MOD17A2H rice–wheat rotation cropland Science Q |
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random forest gross primary productivity eddy covariance MOD17A2H rice–wheat rotation cropland Science Q 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 |
| 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. |
| format |
article |
| 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 |
| 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_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_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_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 |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/32470ff64b074589acd4d81a34a23e3e |
| work_keys_str_mv |
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