文献类型： 外文期刊
作者： Zhao, Yu 1 ; Han, Shaoyu 1 ; Zheng, Jie 1 ; Xue, Hanyu 1 ; Li, Zhenhai 1 ; Meng, Yang 1 ; Li, Xuguang 5 ; Yang, Xiaodong 1 ; Li, Zhenhong 6 ; Cai, Shuhong 5 ; Yang, Guijun 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
2.Shanxi Agr Univ, Coll Agr, Taigu 030801, Shanxi, Peoples R China
3.Henan Acad Agr Sci, Inst Agr Econ & Informat, Zhengzhou 450002, Henan, Peoples R China
4.Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China
5.Cultivated Land Monitoring & Protect Ctr Hebei, Shijiazhuang 050056, Peoples R China
6.Changan Univ, Sch Geol Engn & Geomat, Xian 710054, Peoples R China
期刊名称：EARTH SYSTEM SCIENCE DATA （ 影响因子：11.4； 五年影响因子：12.2 ）
ISSN： 1866-3508
年卷期： 2023 年 15 卷 9 期
页码：
收录情况： SCI
摘要： Generating spatial crop yield information is of great significance for academic research and guiding agricultural policy. Existing public yield datasets have a coarse spatial resolution, spanning from 1 to 43 km. Although these datasets are useful for analyzing large-scale temporal and spatial change in yield, they cannot deal with small-scale spatial heterogeneity, which happens to be the most significant characteristic of the Chinese farmers' economy. Hence, we generated a 30 m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter-wheat-producing provinces in China for the period 2016-2021 with a semi-mechanistic model (hierarchical linear model, HLM). The yield prediction model was built by considering the wheat growth status and climatic factors. It can estimate wheat yield with excellent accuracy and low cost using a combination of satellite observations and regional meteorological information (i.e., Landsat 8, Sentinel 2 and ERA5 data from the Google Earth Engine (GEE) platform). The results were validated using in situ measurements and census statistics and indicated a stable performance of the HLM based on calibration datasets across China, with a correlation coefficient (r) of 0.81 and a relative root mean square error (rRMSE) of 12.59 %. With regards to validation, the ChinaWheatYield30m dataset was highly consistent with in situ measurement data and statistical data (p<0.01), indicated by an r (rRMSE) of 0.72** (15.34 %) and 0.69** (19.16 %). The ChinaWheatYield30m is a sophisticated dataset with both high spatial resolution and excellent accuracy; such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling and regional climate evaluation. The ChinaWheatYield30m dataset generated from this study can be downloaded from 10.5281/zenodo.7360753 (Zhao et al., 2022b).
- 相关文献
作者其他论文 更多>>
-
Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data
作者：Liu, Yang;Feng, Haikuan;Fan, Yiguang;Chen, Riqiang;Bian, Mingbo;Ma, Yanpeng;Li, Jingbo;Xu, Bo;Yang, Guijun;Liu, Yang;Liu, Yang;Feng, Haikuan;Yue, Jibo;Jin, Xiuliang
关键词：AGB; Hyperspectral features; Deep features; SPA; LSTM; PLSR
-
Improving potato above ground biomass estimation combining hyperspectral data and harmonic decomposition techniques
作者：Liu, Yang;Feng, Haikuan;Fan, Yiguang;Chen, Riqiang;Ma, Yanpeng;Bian, Mingbo;Yang, Guijun;Liu, Yang;Liu, Yang;Feng, Haikuan;Yue, Jibo
关键词：AGB; ASD; UHD185; Harmonic components; PLSR
-
Hyperspectral Estimation of Chlorophyll Content in Grape Leaves Based on Fractional-Order Differentiation and Random Forest Algorithm
作者：Li, Yafeng;Xu, Xingang;Zhu, Yaohui;Xue, Hanyu;Li, Yafeng;Xu, Xingang;Wu, Wenbiao;Yang, Guijun;Yang, Xiaodong;Meng, Yang;Jiang, Xiangtai;Xue, Hanyu
关键词：different varieties of grapes; leaf chlorophyll content; hyperspectral remote sensing; data-processing; RFR
-
A model suitable for estimating above-ground biomass of potatoes at different regional levels
作者：Liu, Yang;Fan, Yiguang;Ma, Yanpeng;Chen, Riqiang;Bian, Mingbo;Yang, Guijun;Feng, Haikuan;Yue, Jibo;Jin, Xiuliang
关键词：Potato; Hierarchical linear model; Hyperspectral; Meteorological data; Biomass
-
Real-time monitoring of maize phenology with the VI-RGS composite index using time-series UAV remote sensing images and meteorological data
作者：Feng, Ziheng;Ma, Xinming;Feng, Ziheng;Cheng, Zhida;Ren, Lipeng;Liu, Bowei;Zhang, Chengjian;Zhao, Dan;Sun, Heguang;Feng, Haikuan;Long, Huiling;Xu, Bo;Yang, Hao;Song, Xiaoyu;Yang, Guijun;Zhao, Chunjiang
关键词：UAV; Real-time; Composite index; Maize phenology; BBCH
-
Removal of canopy shadows improved retrieval accuracy of individual apple tree crowns LAI and chlorophyll content using UAV multispectral imagery and PROSAIL model
作者：Zhang, Chengjian;Chen, Zhibo;Chen, Riqiang;Qi, Ning;Zhang, Wenjie;Yang, Hao;Zhang, Chengjian;Yang, Guijun;Xu, Bo;Feng, Haikuan;Chen, Riqiang;Qi, Ning;Zhang, Wenjie;Zhao, Dan;Yang, Hao;Zhao, Dan;Cheng, Jinpeng
关键词：Leaf area index (LAI); Leaf chlorophyll content (LCC); Canopy chlorophyll content (CCC); Broad -band vegetation indexes (VIs); A hybrid inversion model
-
Recognition of wheat rusts in a field environment based on improved DenseNet
作者：Chang, Shenglong;Cheng, Jinpeng;Fan, Zehua;Ma, Xinming;Li, Yong;Zhao, Chunjiang;Chang, Shenglong;Yang, Guijun;Cheng, Jinpeng;Fan, Zehua;Yang, Xiaodong;Zhao, Chunjiang
关键词：Plant disease; Wheat rust; Image processing; Deep learning; Computer vision (CV); DenseNet
