7 August 2024 Accurate soil moisture retrieval using Cyclone Global Navigation Satellite System delay Doppler maps by random forest regression algorithm based on multi-characteristic variable combination
Naiquan Zheng, Hongzhou Chai, Qiankun Zhang, Zhihao Wang, Honglei Ma
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Abstract

Soil moisture (SM) is a crucial meteorological parameter affecting agricultural production and ecosystems, so accurately capturing SM is significant for farm irrigation and ecosystem protection. The delay Doppler maps data of the Cyclone Global Navigation Satellite System (CYGNSS) contains characteristic information related to SM, providing a new observation method for SM observation. First, the CYGNSS data are matched to the soil moisture active and passive dataset of 36 km×36 km grid by the nearest neighbor method. Then, a random forest regression (RFR) model is constructed for training and prediction. It is found that the RFR model based on the combination of 13 characteristic variables can accurately predict SM. On the training set, the correlation coefficient (R) of the model is 0.966, the root mean square error (RMSE) is 0.026 cm3cm3, and the mean error (ME) is 0.000 cm3cm3. On the test set, the R of the model is 0.903, the RMSE is 0.041 cm3cm3, and the ME is 0.001 cm3cm3. Simultaneously, to study the advantages of RFR prediction, this study also compared it with multiple linear regression (MLR) predictions. The results show that the RFR algorithm has higher accuracy both in the training set and the test set. Compared with MLR, R increased by 26.3% in the training set and RMSE decreased by 59.4%. R increased by 17.1% in the test set, and RMSE decreased by 35.9%. This demonstrates that the RFR model with multi-characteristic variables has good reliability, making it an efficient way to use satellite remote sensing data for SM retrieval.

© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE)
Naiquan Zheng, Hongzhou Chai, Qiankun Zhang, Zhihao Wang, and Honglei Ma "Accurate soil moisture retrieval using Cyclone Global Navigation Satellite System delay Doppler maps by random forest regression algorithm based on multi-characteristic variable combination," Journal of Applied Remote Sensing 18(3), 034510 (7 August 2024). https://doi.org/10.1117/1.JRS.18.034510
Received: 1 February 2024; Accepted: 8 July 2024; Published: 7 August 2024
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KEYWORDS
Education and training

Satellites

Soil moisture

Data modeling

Quality control

Reflection

Doppler effect

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