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Generating surface soil moisture at the 30 m resolution in grape-growing areas based on stacked ensemble learning

文献类型：期刊论文

作者Tao, Shiyu7,8; Zhang, Xia6; Chen, Jingming3,4; Zhang, Zhaoying2,7,8; Kang, Xiaoyan1; Qi, Wenchao6; Wang, Yibo5,6; Gao, Yi5,6
刊名INTERNATIONAL JOURNAL OF REMOTE SENSING
出版日期2024-08-17
卷号45期号:16页码:5385-5424
关键词Grape drought Data fusion Soil moisture Machine learning
ISSN号0143-1161
DOI10.1080/01431161.2024.2377228
英文摘要Accurate and timely monitoring of drought conditions in grape-producing regions is crucial for achieving healthy growth of grapes. Current soil moisture (SM) products are primarily available at coarse resolutions (e.g. several to tens of kilometres), constraining its applications at fine scales. Here, we trained a weighted stacking ensemble model including three tree-based models (categorical boosting, random forest, and gradient boosting decision tree), using seven forcing parameters related to spectral reflectance (SR), land surface temperature (LST), and evapotranspiration (ET), in conjunction with the digital elevation model (DEM) feature. The weighted stacking ensemble model exhibited an average R2 of 0.86 and an average RMSE of 0.021 m3/m3 in simulating SM in the vegetive stage and the mid-ripening stage of grape. Then we generated high spatiotemporal downscaled SM (HSM) data at a grape growing area at high spatiotemporal resolutions (30 m, 8-day) from 2009 to 2018. Our HSM dataset demonstrated strong spatial, seasonal and interannual dynamics that align with 500 m SM dataset derived from single MODIS data, and the HSM dataset shows more details in SM distribution. Additionally, the SM time series in the HSM is consistently correlated with drought events, offering intricate spatiotemporal information for drought monitoring. The application of downscaled SM results identified a concentration of drought events in the eastern foothills of the Helan Mountains, particularly severe drought conditions were observed in the Hongsipu production area. Drought occurrences in the Hongsipu production area ranged from 90% to 91% during May and June, decreasing to 73% and 41% in July and August, respectively. These findings significantly contribute to enhancing high spatiotemporal SM monitoring capabilities, offering valuable guidance for timely water management in grape-growing regions.
WOS关键词DIFFERENCE WATER INDEX ; LANDSAT 8 DATA ; TIME-SERIES ; DATA FUSION ; VEGETATION INDEX ; STRESS INDEX ; LATE FROST ; MODIS ; DROUGHT ; MODEL
资助项目National Natural Science Foundation of China[42371360] ; Chinese Academy of Sciences Strategic Leading Science and Technology Project[XDA28080502] ; Verification of carbon dioxide accuracy of carbon satellite based on site flux[nmqxydcx202215]
WOS研究方向Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001273897700001
出版者TAYLOR & FRANCIS LTD
资助机构National Natural Science Foundation of China ; Chinese Academy of Sciences Strategic Leading Science and Technology Project ; Verification of carbon dioxide accuracy of carbon satellite based on site flux
源URL[http://ir.igsnrr.ac.cn/handle/311030/206996]  
专题生态系统网络观测与模拟院重点实验室_外文论文
通讯作者Zhang, Xia
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China
2.Nanjing Univ, Nanjing, Peoples R China
3.Fujian Normal Univ, Sch Geog Sci, Fuzhou, Peoples R China
4.Univ Toronto, Dept Geog & Planning, Toronto, ON, Canada
5.Chinese Acad Sci, Univ Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China
6.Chinese Acad Sci, Aerosp Informat Res Inst, 20 Datun Rd, Beijing 100101, Peoples R China
7.Nanjing Univ, Sch Geog & Ocean Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Key Lab Land Satellite Remote Sensing Applicat,Min, Nanjing, Jiangsu, Peoples R China
8.Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714		 Tao, Shiyu,Zhang, Xia,Chen, Jingming,et al. Generating surface soil moisture at the 30 m resolution in grape-growing areas based on stacked ensemble learning[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2024,45(16):5385-5424.
APA Tao, Shiyu.,Zhang, Xia.,Chen, Jingming.,Zhang, Zhaoying.,Kang, Xiaoyan.,...&Gao, Yi.(2024).Generating surface soil moisture at the 30 m resolution in grape-growing areas based on stacked ensemble learning.INTERNATIONAL JOURNAL OF REMOTE SENSING,45(16),5385-5424.
MLA Tao, Shiyu,et al."Generating surface soil moisture at the 30 m resolution in grape-growing areas based on stacked ensemble learning".INTERNATIONAL JOURNAL OF REMOTE SENSING 45.16(2024):5385-5424.

入库方式： OAI收割

来源：地理科学与资源研究所

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