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Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery

Lookup NU author(s): Professor Zhenhong Li

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. In the Sentinel-1 approach, several enhanced radar indices were constructed by Sentinel-1 backscatter coefficient of imagery, and selected the one that was most sensitive to soil water content as the input parameter of a water cloud model. Finally, a water content inversion model for winter wheat crop was established. In the Sentinel-2 approach, the gray relational analysis was used for several optical vegetation indices constructed by Sentinel-2 spectral feature of imagery, and three vegetation indices were selected for multiple linear regression modeling to retrieve the wheat crop water content. 58 ground samples were utilized in modeling and verification. The water content inversion model based on Sentinel-2 optical images exhibited higher verification accuracy (R = 0.632, RMSE = 0.021 and nRMSE = 19.65%) than the inversion model based on Sentinel-1 SAR (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%). This study provides a reference for estimating the water content of wheat crops using data from the Sentinel series of satellites.


Publication metadata

Author(s): Han D, Liu S, Du Y, Xie X, Fan L, Lei L, Li Z, Yang H, Yang G

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2019

Volume: 19

Issue: 18

Online publication date: 17/09/2019

Acceptance date: 09/09/2019

Date deposited: 01/10/2019

ISSN (print): 1424-8239

ISSN (electronic): 1424-8220

Publisher: MDPI AG

URL: https://doi.org/10.3390/s19184013

DOI: 10.3390/s19184013

PubMed id: 31533327


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