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Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images

文献类型： 外文期刊

作者： Yang, Songlin 1 ; Li, Shanshan 3 ; Zhang, Bing 1 ; Yu, Ruyi 4 ; Li, Cunjun 5 ; Hu, Jinkang 1 ; Liu, Shengwei 1 ; Cheng, Enhui 1 ; Lou, Zihang 1 ; Peng, Dailiang 1 ;

作者机构： 1.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing, Peoples R China

2.Int Res Ctr Big Data Sustainable Dev Goals, Beijing, Peoples R China

3.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing, Peoples R China

4.Chinese Acad Sci, Aerosp Informat Res Inst, China Remote Sensing Satellite Ground Stn, Beijing, Peoples R China

5.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing, Peoples R China

关键词： fractional vegetation cover; winter wheat; UAS; remote sensing; machine learning

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.6； 五年影响因子：6.8 ）

ISSN： 1664-462X

年卷期： 2023 年 14 卷

页码：

收录情况： SCI

摘要： Accurate estimation of fractional vegetation cover (FVC) is essential for crop growth monitoring. Currently, satellite remote sensing monitoring remains one of the most effective methods for the estimation of crop FVC. However, due to the significant difference in scale between the coarse resolution of satellite images and the scale of measurable data on the ground, there are significant uncertainties and errors in estimating crop FVC. Here, we adopt a Strategy of Upscaling-Downscaling operations for unmanned aerial systems (UAS) and satellite data collected during 2 growing seasons of winter wheat, respectively, using backpropagation neural networks (BPNN) as support to fully bridge this scale gap using highly accurate the UAS-derived FVC (FVCUAS) to obtain wheat accurate FVC. Through validation with an independent dataset, the BPNN model predicted FVC with an RMSE of 0.059, which is 11.9% to 25.3% lower than commonly used Long Short-Term Memory (LSTM), Random Forest Regression (RFR), and traditional Normalized Difference Vegetation Index-based method (NDVI-based) models. Moreover, all those models achieved improved estimation accuracy with the Strategy of Upscaling-Downscaling, as compared to only upscaling UAS data. Our results demonstrate that: (1) establishing a nonlinear relationship between FVCUAS and satellite data enables accurate estimation of FVC over larger regions, with the strong support of machine learning capabilities. (2) Employing the Strategy of Upscaling-Downscaling is an effective strategy that can improve the accuracy of FVC estimation, in the collaborative use of UAS and satellite data, especially in the boundary area of the wheat field. This has significant implications for accurate FVC estimation for winter wheat, providing a reference for the estimation of other surface parameters and the collaborative application of multisource data.

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