{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T06:21:51Z","timestamp":1772605311168,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U2243203"],"award-info":[{"award-number":["U2243203"]}]},{"name":"National Natural Science Foundation of China","award":["41971374"],"award-info":[{"award-number":["41971374"]}]},{"name":"National Natural Science Foundation of China","award":["41807173"],"award-info":[{"award-number":["41807173"]}]},{"name":"National Natural Science Foundation of China","award":["ST202228"],"award-info":[{"award-number":["ST202228"]}]},{"name":"Carbon Peak and Carbon Neutralization Key Science and Technology Program of Suzhou","award":["U2243203"],"award-info":[{"award-number":["U2243203"]}]},{"name":"Carbon Peak and Carbon Neutralization Key Science and Technology Program of Suzhou","award":["41971374"],"award-info":[{"award-number":["41971374"]}]},{"name":"Carbon Peak and Carbon Neutralization Key Science and Technology Program of Suzhou","award":["41807173"],"award-info":[{"award-number":["41807173"]}]},{"name":"Carbon Peak and Carbon Neutralization Key Science and Technology Program of Suzhou","award":["ST202228"],"award-info":[{"award-number":["ST202228"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001\u20132018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm\u22122yr\u22121 across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP.<\/jats:p>","DOI":"10.3390\/rs15194693","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T02:31:29Z","timestamp":1695695489000},"page":"4693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Non-Ignorable Differences in NIRv-Based Estimations of Gross Primary Productivity Considering Land Cover Change and Discrepancies in Multisource Products"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4067-298X","authenticated-orcid":false,"given":"Jiaxin","family":"Jin","sequence":"first","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China"},{"name":"Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 210024, China"}]},{"given":"Weiye","family":"Hou","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4642-4701","authenticated-orcid":false,"given":"Longhao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China"},{"name":"Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Songhan","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Key Laboratory of Crop Physiology and Ecology in Southern China, College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[{"name":"Tourism and Social Administration College, Nanjing Xiaozhuang University, Nanjing 211171, China"}]},{"given":"Qiuan","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China"}]},{"given":"Xiuqin","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3329-5787","authenticated-orcid":false,"given":"Liliang","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2019.01.016","article-title":"What is global photosynthesis? 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