Enhancing surface soil moisture estimation in agricultural fields: a combined approach of improved WCM and CNN

Ran Wang; Jianhui Zhao; Ning Li

doi:10.1117/12.3010239

15 November 2023 Enhancing surface soil moisture estimation in agricultural fields: a combined approach of improved WCM and CNN

Ran Wang, Jianhui Zhao, Ning Li

Author Affiliations +

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 128151Q (2023) https://doi.org/10.1117/12.3010239
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

Real-time and dynamic monitoring of soil moisture is critically essential for farming activities and crop yield estimation. This paper focuses on the study of surface soil moisture (SSM) in agricultural fields with different vegetation sparsity. Based on the improved water cloud model (WCM) and convolutional neural network (CNN), the SSM inversion of agricultural fields covered with winter wheat at different growth stages was achieved. Firstly, the vegetation cover factor was introduced into the WCM to separate the scattering contribution under crop cover from the direct scattering contribution from the bare ground. Then, the remote sensing data was used to derive numerous characteristic parameters to the SSM. Finally, the CNN model was established for the purpose of SSM inversion. The better correlation between the backscattering coefficients obtained by the improved WCM simulations and the measured SSM was shown. The coefficients of determination were 0.46 and 0.39 under VV and VH polarization, respectively. Better inversion accuracy can be obtained by combining the two parameters with other characteristic parameters as input data into the CNN model for SSM inversion. The coefficient of determination was 0.75, root mean square error was 2.51 vol.% and mean absolute error was 2.12 vol.%. Therefore, the method can effectively separate the effects of agricultural crops and bare ground on radar signals, which contributes a novel research approach and guidance for handling SSM inversion in agricultural fields covered by vegetation.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Ran Wang, Jianhui Zhao, and Ning Li "Enhancing surface soil moisture estimation in agricultural fields: a combined approach of improved WCM and CNN", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 128151Q (15 November 2023); https://doi.org/10.1117/12.3010239

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KEYWORDS

Data modeling

Vegetation

Agriculture

Backscatter

Soil moisture

Deep learning

Scattering

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