{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T20:59:45Z","timestamp":1779137985008,"version":"3.51.4"},"reference-count":61,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Despite advances in remote sensing\u2013based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPPMOD) is less well understood over rice\u2013wheat-rotation cropland. To improve the performance of GPPMOD, a random forest (RF) machine learning model was constructed and employed over the rice\u2013wheat double-cropping fields of eastern China. The RF-derived GPP (GPPRF) agreed well with the eddy covariance (EC)-derived GPP (GPPEC), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m\u22122 d\u22121. Therefore, it was deemed reliable to upscale GPPEC to regional scales through the RF model. The upscaled cumulative seasonal GPPRF was higher for rice (924 g C m\u22122) than that for wheat (532 g C m\u22122). By comparing GPPMOD and GPPEC, we found that GPPMOD performed well during the crop rotation periods but underestimated GPP during the rice\/wheat active growth seasons. Furthermore, GPPMOD was calibrated by GPPRF, and the error range of GPPMOD (GPPRF minus GPPMOD) was found to be 2.5\u20133.25 g C m\u22122 d\u22121 for rice and 0.75\u20131.25 g C m\u22122 d\u22121 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.<\/jats:p>","DOI":"10.3390\/rs13214229","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T23:27:39Z","timestamp":1634858859000},"page":"4229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Estimating Gross Primary Productivity (GPP) over Rice\u2013Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product"],"prefix":"10.3390","volume":"13","author":[{"given":"Zexia","family":"Duan","sequence":"first","affiliation":[{"name":"Climate 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, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3486-6286","authenticated-orcid":false,"given":"Yuanjian","family":"Yang","sequence":"additional","affiliation":[{"name":"Climate 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, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaohui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Climate 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, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiu","family":"Gao","sequence":"additional","affiliation":[{"name":"Climate 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, China"},{"name":"State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lian","family":"Zong","sequence":"additional","affiliation":[{"name":"Climate 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, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sihui","family":"Fan","sequence":"additional","affiliation":[{"name":"Ningbo Meteorological Bureau, Ningbo 315000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yin","sequence":"additional","affiliation":[{"name":"Climate 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, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhu, X.Y., Pei, Y.Y., Zheng, Z.P., Dong, J.W., Zhang, Y., Wang, J.B., Chen, L.J., Doughty, R.B., Zhang, G.L., and Xiao, X.M. 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