文献类型： 外文期刊
作者： Tan, Yunlong 1 ; Cheng, Enhui 2 ; Feng, Xuxiang 3 ; Zhao, Bin 6 ; Chen, Junjie 1 ; Xie, Qiaoyun 7 ; Peng, Hao 4 ; Li, Cunjun 9 ; Lu, Chuang 9 ; Li, Yong 10 ; Zhang, Bing 3 ; Peng, Dailiang 2 ;
作者机构： 1.Henan Polytech Univ, Sch Surveg & Land Informat Engn, Jiaozuo, Peoples R China
2.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing, Peoples R China
5.Int Res Ctr Big Data Sustainable Dev Goals, Beijing, Peoples R China
6.Shandong Agr Univ, Sch Informat Sci & Engn, Tai An, Peoples R China
7.Univ Western Australia, Sch Engn, Perth, WA, Australia
8.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Urumqi, Xinjiang, Peoples R China
9.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing, Peoples R China
10.Shandong Agr Univ, Natl Key Lab Wheat Improvement, Tai An, Peoples R China
11.Shandong Agr Univ, Coll Agron, Tai An, Peoples R China
关键词： winter wheat; vegetation index; remote sensing; growth monitoring; cultivated land management
期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）
ISSN： 1664-462X
年卷期： 2024 年 15 卷
页码：
收录情况： SCI
摘要： In the past, the use of remote sensing for winter wheat growth monitoring mainly relied on the relative growth assessment of a single vegetation index, such as normalized Vegetation index (NDVI). This study advanced the methodology by integrating field-measured data with Sentinel-2 data. In addition to NDVI, it innovatively incorporated two parameters, aboveground biomass (AGB) and leaf area index (LAI), for a more comprehensive relative growth assessment. Furthermore, the study employed the agricultural production systems simulator (APSIM) model to use LAI and AGB for absolute growth monitoring. The results showed that the simulated LAI and AGB closely match the field-measured values throughout the entire growth period of winter wheat under various conditions (R-2 > 0.9). For relative growth monitoring, NDVI showed significant linear positive correlations (r > 0.74 and P< 0.05) with both LAI and AGB simulated by the APSIM model. Overall, this research shows that LAI and AGB obtained from the APSIM model provide a more detailed and accurate approach to monitoring of winter wheat growth. This improved monitoring capability can support effective land management arable and provide technical guidance to advance precision agriculture practices.
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